39 Burst results for "AI"

Fresh update on "ai" discussed on Mayo Clinic Q&A

Mayo Clinic Q&A

01:26 min | 4 hrs ago

Fresh update on "ai" discussed on Mayo Clinic Q&A

"Artificial intelligence playing a role. The role of efficient intelligence in industry is huge. And it's coming to clinical cat now. This is really translating kind of facial recognition technology that we saw it with with google but instead of recognizing faces i recognize polyps and may during a live colonoscopy will detect even small in central polyps as drawer green oblique box random to draw attention to you this could lead to increase detection about two fifty percent even for experts in does this is really a. I think a game changer for us cash. That's fascinating so can you tell me. Do we understand Which polyps will likely change from being benign to becoming cancerous. We've understood prolonged times of more than thirty years. This idea of the costs number sequence so this is the classic polyps the perhaps look like middle cherries on a stool. They're red and they're easy to see. Mickey sniff them off that starts in developing into cancer in the future however we found that when we were doing kernels to be reworked preventing as much cad says as we expected particularly in the right colour and it turns out that a group of poets which historically we've ignored which used to be called hypoplastic oats now cooled sarrazin on us because when you look under the microscope that they have a soltys hedge these poets how subtle that flat that almost see through and they occur in the right colonel the place where we thought we we will fade to red cancer. Suddenly now appreciating these polyps with seeing detecting and respecting them. And i think that's a development in the last ten years is likely to improve the prevention of cancer in the right colonel. What type of surveillance is needed after. An initial screening. So if it depends on the test that you have but for skippy. The recommendation is to have a cows every ten years. If you have if you have a it politically if you have multiple poets will lodge polyps then you need to come back earlier. Maybe five years or even trees it if you hadn't a precancerous polyp because we those patients are more likely to develop a reporting in a cancer in the future. What about what about genetics. Does genetics play a role in who develops colorectal. Cancer is is it hereditary. I think so. Genetic suddenly plays a big role maybe more than many other cancers most al kansas on directly genetically related but we will carry some genes that make us more or less likely to get bowel cancer in the same way we can carry a range of genes that determine high tool or i count so we get a mixture genes from our parents. You have a first degree relative a parenteral or other sister. You has vowed cancer particularly young age. You you probably a higher risk of cancer have many more intensive screening however there are very small group of people you have a single gene defect. The best night of these is probably lynch. The named after us physicians henry lynch and this is essentially a an era in the in the enzymes that proofread dna. So when we're replicating dna at they sniff out the heiress. And if that's not working you got lots of dna irs. Because you attornal replicates dna very frequently your significantly increased risk of developing cancer patients would have even every two years so much more intensive screening if you carry one of these gene defects and now every time we diagnose about cancer we will check the cancer to see whether it's been driven i by that by that all right so talk about colonoscopy prep. How soon should you begin to prep your diet. Komo's a difficult thing and it probably nowadays is like least about having a code. But i think with with the mown bowker open you can just stop in a day before you each to commence you're starting to have what what is predominantly a very low fiber diet a starkly in a week we'd being told and even to prevent bound so we need to to hifi dot just at this moment to get about Fought fiber is enemy. We count cleared away even with suction when we're doing procedure. So the minimum amount of fiber which okay white rice white red Skinless chicken on to to prepare ourselves to get our what about caffeine dairy products. Things like that caffeine. It is fun. If you having black koffi. Oval black t. their reports less good because the the preaching in that Chemicals trouble if you wanted a tiny bit of can you not. That's not to sell things but probably not a big pizza Cheese thank god dr. Do you have any advice to make it easier for people to prep for their colonoscopy. Like you said it's it's probably everyone's least favorite thing so it is a challenge Many of the preps now comes quite high bullying that may be. Tv too sometimes even fully to drink so so putting that making it up beforehand baking powder before hitting it in the fridge getting cold sometimes he puts in citrus stuff in it. It makes it a bit more power to those and counter-intuitively the Works best if you drink addition water even on talk of what you've been given already which seems like a big bully so extra fluids alongside. There is some evidence that exercise can improve kilometer utilities. Don't sit around it beyond about the other big thing that's changed in the last few years that the patients will notice is the idea of splitting the he has some in evening. And some in the morning of your housekeeping and if you will be eight o'clock that might mean five o'clock in the morning to take that last. I surprise. it seems a really hard tough thing to do to patient. This is your once in ten years examination to try and find the polyps again attendant out kanza. So at least if your doctor aussie discovered it. I said this is one of those times natalie. Salt as needed. Just gotta power through. Those are some good tips though. Okay finally. let's talk about prevention What steps can we take to reduce our risk of developing colon cancer. They're injured myself things. We can do particularly adopting a mediterranean style. it is helpful so that's less resumes. Bless process me all knots and more fruit and vegetables other things that are that the some three to exercise uncertainly. Obesity is a big risk factor for many kansas including about kansas obesity. Wages helpful. Smoking is an obvious improbably reductions in in combined with colonoscopy a why we're seeing reductions it in outcomes rates in over americans..

Cancer Sarrazin Red Cancer Al Kansas Henry Lynch Mickey Colon Cancer Koffi Komo Google Lynch IRS Natalie Mediterranean Obesity Kansas
Fresh update on "ai" discussed on Mayo Clinic Q&A

Mayo Clinic Q&A

01:26 min | 4 hrs ago

Fresh update on "ai" discussed on Mayo Clinic Q&A

"Coming up on mayo clinic. Qna and there are wide reggie actions now for about kansas screening all of which provide substantial protection against bow kansas. Kansas actually extremely unusual cancers in theory almost completely preventable and regular screening is a key part that prevention finding detecting the cancer early increases the chance for a full recovery. I think this is really a time when engagement without kansas screening. We could really push cancer down to welcome everyone to mayo clinic. I'm deepen sitting in for dr helena's alka. According to the world health organization colorectal cancer is the third most common cancer worldwide. It accounts for almost two million new cancer cases each year colorectal cancer which is also known as bowel. Cancer typically affects older adults. Although it can happen at any age screening for colorectal cancer is important to identify precancerous polyps that could develop into cancer and there are several screening options now available to patients joining us to discuss. Is dr james east gastroenterologist at mayo clinic health care in london. Welcome to the program. Dr eastern. thank you so much joining us. So can you tell us. About advances in colon. cancer screening What options are available and how to patients. No which one is right for them. I think when you're thinking about the option that's right. The the key thing is that the best test is the one that you're willing to do. There's no point in in in being setup for be if you're not willing to comfort and are wide reggie functions now for about kansas screening. All all of which provide substantial protection against bow kanza. Kansas actually extremely unusual cancers in in the in theory. It's almost completely preventable. By high quality screening examinations in terms of the options available at the moment the most commonly recognised one is busy which has the advantage of by detecting early kansas at Curable stage but also in finding and removing precancerous polyps. Stokes cancelled about pig. Over the next five to ten years. We also have flexible sigmoidoscopy which has been popular in the united kingdom which examines a just the lower third of how this is often used it. Impatience perhaps it in the fifties and sixties and at these ages this is web where precancerous polyps a pig it in the low part of the lodge out a meth laura in range for this test of the famine raped well those who would prefer a less invasive test We can now do Fecal munich Community chemical testing the cycles fit tests. This is a sensitive test for blood and still and if if blood is found then we proceed to the ranger. Be for that patient. I i'm not going to be done in the privacy of your own home. Often done every one to two years as an add on to that with using stool dna. This is a test code. Got this was developed for mayor researches now provided by exact sciences and is available in the. Us has improved test before final tests. That that's big recently. Recommended at is c. T. code inaugur. Affi so this is a cat scan of the abdomen but set up in a special way outweigh you have some gaspard into the bow to stretch about. And then you have a scan lying flat on your back in a flash on your tummy. And this is almost as good as komo's be at detecting cancer logic poets so this really quite a wide range of screening options for patients to pick from and discuss with that. Dr yeah. i'll say it's good to have so many different options. Can you tell us. Are there differences between the us and the uk when it comes to training recommendations so the the us colonoscopy has been very popular because as opposed describing has this powerful preventive effect on on bowel cancer over the entire out absolutely needs to be repeated if it's clear every ten years however it's invasive on there is a customer location today matt for the whole populations in the u. k. The national health service is chosen to use fit testing the test of let him stew Given out every a tease the in that's currently for majors. Sixty to seventy four. Although the age for that's going to decrease in england to fifty minute future. I should just say the big news for kettles could be another screening tests in the us. Is that the screening. Age has been decreased from fifty to forty five from when you should start screening initiative by the american cancer society earlier this year. This this is being taken up by the societies because we we've seen data that certainly in western calculations were seeing cancer developing at a younger age. Gotcha very important knelt. Thanks for bringing that up. How about screening rates does one country do better than the other As far as getting their population screened for colorectal cancer. The message in the us has kind of go out there and streaming engagement komansky as equally in the u. We see gauge levels particularly with the fit test which is like easier to do than the older kabul choir tests that that we're seeing engagement rates of sixty or seventy percent but seventy some populations are honda to reach in in deprived areas and from some minority groups a worldwide We're seeing low streaming engagement. I'm particularly for some patients in the middle east which is particularly golden visit. They seem to have a particularly high rates about cancer at a young age. Now i understand that research is looking at ways to better detect polyps how is.

Cancer Colorectal Cancer Kansas Mayo Clinic Dr Helena Common Cancer Reggie Dr James Mayo Clinic Health Care Affi World Health Organization Bowel Stokes UK United States Gaspard Komo London National Health Service
Fresh update on "ai" discussed on Elevate: The Official Podcast of Elite Agent Magazine

Elevate: The Official Podcast of Elite Agent Magazine

00:40 min | 8 hrs ago

Fresh update on "ai" discussed on Elevate: The Official Podcast of Elite Agent Magazine

"While literally like we can possibly plug that in today. Right and But you know where it gets dumped. His wife woolen gobbo. Or yeah it's like it's like seeing to The boy george voice on ways you know he counts. Say the straight names but he can say you know Make chen yeah absolutely and and you know what. That's that's the problem right. Is this the english. Language is a summit nuance And i'm sure at some point in time it they'll be some of the things that had had localize it like really legalize it But it's probably a long way off by say lots of tools out there. Don't get lost in them. I mean even the mail tells now like google and a few if you got into the google platforms or even lex from amazon. You to build your moles like plug and play nightside needed. If you've got data if you gotta spend sheet in chuck it in you can play around with it and if you do think that predictive analytics is something about apply with selling investing Encourage it actually say i can see some of the big groups now are investing in india some questionable areas like in my opinion the pices that you should invest in the position invest. I probably dot is really important. Mike you show that you've got mine. Time data and the most important first party data in my opinion is not nine thousand. Fine numbers and addresses. It is consent and nuts. The.

Woolen Gobbo Chen Google George Amazon Chuck India Mike
Fresh update on "ai" discussed on Elevate: The Official Podcast of Elite Agent Magazine

Elevate: The Official Podcast of Elite Agent Magazine

01:04 min | 9 hrs ago

Fresh update on "ai" discussed on Elevate: The Official Podcast of Elite Agent Magazine

"But then you know obviously there's a time constraint to skyline that and schuyler is the challenge and we always knew that eventually we would want really to have a more active role in that process of of and even though it's called prospecting today lodge ran prospecting. We've always come at it from a level of service if that makes sense so interesting about compensation is being away. To prospect we think about conversations being righted deliver guide service and enabling right service. Because and that's important for us so that's probably the the big thing that she's lead how to do in the last twelve months even is she had lost fifty four thousand independent compensations without a human being volved. She's land to talk like any normal kid. Absolutely just scale kind of weird isn't it. Yeah yeah. I mean you guys that rachel in the first place with cleveland marketing. So say we'll real truly. Think of raiders. Just passing now. Well you know it's important as actually lesson in the first. Six months of the company was before it was just a. I was a piece of software and it was sorry frustrating because when he sat down in front of the industry and what i mean by that is sat down in front of people. Say we'll help me help you with software. They just want to talk about right. And i just wanted. There was just compensation around is in what that meant and Which was not the conversations. The compensation was bought are the constraints that you have today. One of the problems that you have today. Iran you'll business and how. How does the concept of the the the opportunity in and ambig- dighton and moberly data science. How does the opportunity in that particular. Because that's enabling technology it's not. It's a group of technologies. It's a specific thing. It's a set of enabling technologies. How does that actually present a potential solution to some problems you have and so as soon as we start talking about talking about a digital employees with it makes sense. It'd be great if they can do this. Earth guided. Thank you do that. Because i hate doing this and i hate doing that or i didn't have enough time to do this. I suck at doing this I didn't care about this but Other people not just humanize. The whole conversation thing is really important for us. Yeah so undertook to you. Bet i in a second. Because i think that's a really interesting topic but i also want to talk about something that i now is near and dear to you and that's paper not using this. Crm's as well as could be expected because you know it's funny and you see it on social meteorology time that i'm age old question of which is the rest crm because You know obviously. It's the sierra me. Us ryan but But you're you are. The case took talk to me about what you see as being an undue use. Crm probably let me car for my position as the industry use doesn't use this year. I think a lot of agents do it's what the diocese is the data that they have the opportunity to having some of that doesn't even sit inside the sierra if that makes sense so a big part of what we do is not just Middle's bringing the ancillary data so data from prospect for example oil..

Schuyler Dighton Raiders Moberly Rachel Cleveland Iran Ryan
"ai" Discussed on AI Today Podcast: Artificial Intelligence Insights, Experts, and Opinion

AI Today Podcast: Artificial Intelligence Insights, Experts, and Opinion

03:52 min | Last week

"ai" Discussed on AI Today Podcast: Artificial Intelligence Insights, Experts, and Opinion

"Have they. Are they not aware of. Read it and things like that. I mean seriously trolls. Just great people crazy people. It's course we'll get into that. I mean like when presented with an ad chat bot that will be trained in random. Did you're gonna throw some crazy stuff at it. Well the reason why i got so far is actually a methodology thing. It's like they didn't ask themselves the question at the very first step. What could go wrong actually. Actually didn't actually. They didn't ask that question at all they. I don't know what they were thinking right. And so in a methodology you have to ask the question of what can go wrong before you even collect your very first bite of data. It's actually part of the business. In the. cpi methodology the the golden responsibly. I- considerations are in the very first space. And it's not just the obvious things like well this system go haywire and in cause harm. There's actually a lot of sub sub issues and this is is something we actually cover in it and we actually have certification. Just focus on ethical and responsible way i. It's called her. The ethical and responsibly. I certification can come up with a better name but but basically it starts with the our our comprehensive ethical framework that we actually have shared actually can go back to another podcast and hear about it. It's available creative commons. You can go on there and you know download it for free and you can but what we do in our certifications we we actually bring people through the ethical from they know how to apply it and then we go over like this is like eight hours actually four separate to our courses. We spent some time in saying. How do you deal with disclosure. How do you deal with consent. How do you do with governance decision making. Is there a human in the loop. How do you determine the humans capability. What do you do about ai. Systems that are not explainable or understandable. How about the issues of of addressing bias and fairness in inclusivity and also. How do you actually do it. And you might say that maybe for certain kinds of systems you you may have to worry about some things that maybe you don't but but it's an ethical framework where at least you're thinking about it from the very first step and it's crazy. How many people are building projects. Don't even think about any of these things before it's too late. They don't think about disclosure consent at all and it's like you don't have to to provide full disclosure or full consent but you have to have at least thought about it and that's what a methodologies for it's just there's no rocket science methodology. It's just here. Are the steps that we follow in every single one of our ai projects and that gives us the confidence that we need that our ai projects are not going to fail. Which is what we are talking about in the series exactly so hopefully by the end of the series you will have a comprehensive understanding of why ai projects failed what we've seen with others as to why they're failing and then what you can do to help. Make sure that your projects do not fail. Which really at the end of the day is following a methodology. So hopefully by you know if we haven't convinced you by the end of this podcast by the end of our use case series where we outlined all ten of them. Hopefully we will have convinced you by then so as mentioned stay tuned for upcoming episodes where we will talk about additional reasons why projects fail including why the roi is not justified. Data quality data quantity issues and more. We also have few podcasts..

ai
"ai" Discussed on AI Today Podcast: Artificial Intelligence Insights, Experts, and Opinion

AI Today Podcast: Artificial Intelligence Insights, Experts, and Opinion

05:56 min | Last week

"ai" Discussed on AI Today Podcast: Artificial Intelligence Insights, Experts, and Opinion

"So as mentioned in this particular episode of the podcast. We're going to focus on one of the ten reasons that. Ai is failing and one of those reasons. One of the very common reasons is as kathleen mention that Your people are treating these. Ai projects like their application development projects. Thinking too much of ai as the functionality but as we have learned is not about the function of the what makes an ai. System work has nothing to do with the code. The code that basically does facial recognition doesn't really do facial recognition. It's actually the data that we used to create the model that basically does the fish recognition. And a lot of. You might be saying we'll no. Yeah i mean. I'm doing a all the time. Of course. the data is determining the function. You know how the system behaves and we say to your to to those folks like well. Then why are you running your projects like thr application development projects if you know that has nothing to do with application development right exactly and so you know. That's a common theme that we've seen in. That's why it's important to bring up and continue to remind people say you know. Don't don't run your projects that way. Because they are going to fail a i we talk about this a lot on our podcast right. That data is what fuels. Ai in data is at the heart of ai need to make sure that you have good clean data as well so you know knowing that. This is Data that is what's driving your ai projects. Then you're going to need to think about it not as an application development but more as a data centric project and so you're going to need to Make sure that you have roles and methodologies in place around that data right so methodologies what we're talking about here so so so getting into fear from from from this perspective methodology. When you think about application development we've had methodologies they're really emerged over the past say two decades but really over the past ten years.

kathleen Ai
Author Craig Stanfill Tells a Compelling Tale in His First Novel 'Terms of Service'

The Eric Metaxas Show

02:32 min | Last month

Author Craig Stanfill Tells a Compelling Tale in His First Novel 'Terms of Service'

"Hey folks As you know this is the eric metaxas show and as you also know whenever you read one of those websites that always says terms of service. Have you seen that. And i just thought to myself. It's so annoying because it could mean anything. Nobody reads it. And i thought what a great title that would be for a book. But i don't have time to write a whole book. So i thought maybe somebody else would and they did. His name is craig stand fill and the title of the book is terms of service. Craig welcome the program. Thank you eric. Okay this is a great title terms of service. Ominous is this a dystopia novel. It is an extremely dystopia. Novel portrays a future that we might be creating where you've got these huge big tech companies that run everything in your life and of course they're watching everything you do. Add the the final turn of the screw is they've got these a is that are constantly watching over your shoulders and monitoring everything you do and that lets them exert an incredible level of control over your life. Okay this i gotta tell you you've got your phd in artificial intelligence in one thousand nine hundred eighty three before. I think there was artificial intelligence practically so you are at least an expert on this subject. Most of us of course no nothing about artificial intelligence. So why does it seem so scary to you and obviously you put it in the novel terms of service which i hope people get a copy of but what do you see that the rest of us wouldn't have a clue about well first of all there's nothing intrinsically evil about it's sort of at some level just another technology it can be used for good or ill but when you develop a new technology with as wide ranging implications ai. You really need to think about. What are the risks associated with that technology. And what could happen now. The important thing to understand about. Ai is in the context of social media and so forth is that it is a force multiplier as they say as an example facebook has only fifteen thousand content moderators. And they've got something like two billion people use their platform on a monthly basis and do the risk to take can't watch everybody they can all. They can watch almost nobody. So what they do. In order to enforce their rains upon you is they let their is most of the work.

Eric Metaxas Craig Eric Facebook
Google Develop AI for Detecting Abnormal Chest X-Rays Using Deep Learning

Daily Tech Headlines

02:09 min | Last month

Google Develop AI for Detecting Abnormal Chest X-Rays Using Deep Learning

"On friday we talked about a nature publication by google. Ai scientists that showed how a deep learning system could detect abnormal chest xrays rays with an accuracy. Rivaling that of professional radiologists. The system only detects whether a chess scan is normal or not and is not trained to detect specific conditions. The goal here is to increase productivity and efficiency of radiologists clinical process. Let's examine some a i x ray. Science first of all how to rays work xrays are a type of radiation energy. Wave that can go through. Relatively thick objects without being absorbed or scattered very much. X rays have shorter wavelengths than visible light which makes them invisible to the human eye for medical applications of vacuum x. Ray tube accelerates electrons to collide with a metal and owed and creates rays these rays are then directed towards the intended target like a broken arm for example and then picked up by digital detectors called image plates on the other side differ body tissues absorb x rays differently so the high amount of calcium in bones for example makes them especially efficient at x ray. Absorption and this highly visible on the image detector soft tissues like lungs are slightly lighter but also visible making x ray and efficient method to diagnose pneumonia or pleural a fusion Which is fluid in the lungs. For example according to this latest nature publication approximately eight hundred and thirty seven million chest. Xrays are obtained yearly worldwide. That is a lot of pictures for radiologists to look at and can lead to longer wait times and diagnosis delays. And of course. This is why there's interest in developing ai. Tools to streamline the process many algorithms have already been developed but are rather aimed at detecting specific problems on an x ray. The google ai. Scientists however developed a deep learning system capable of sorting chest xrays into either normal or abnormal data intending. To lighten the case load on radiologists

Chess Google Pneumonia
LA Clippers to Announce Partnership During Groundbreaking for Intuit Dome

Motley Fool Money

00:21 sec | Last month

LA Clippers to Announce Partnership During Groundbreaking for Intuit Dome

"Into. It's going to have a dome. Because steve bomber former microsoft ceo. Who owns the los angeles clippers clippers announced. They've got this brand new home. They're building starting in. I believe twenty twenty three and the naming rights have been purchased by into it so the clippers are going to be playing at the into it. Dome who doesn't want dome

Steve Bomber Los Angeles Clippers Clippers Microsoft Clippers
Generating SQL [Database Queries] From Natural Language With Yanshuai Cao

The TWIML AI Podcast

01:58 min | Last month

Generating SQL [Database Queries] From Natural Language With Yanshuai Cao

"So tell us a little. Bit about touring and the motivation for it. How did the project get started right. So is this natural. Language database interface is a demo of anguish database interface built. And it's really just putting a lot of our word on some parsing space together. In this academic demo so netra language database interface the from application perspective the pin uses to a law a nontechnical users to interact with structured data. Set is there's lots of inside endure and You know who want to give out change for nontechnical users to to get those insights and from a research perspective. It's a very challenging natural english Problem because the underlying problem is you have to parse pasta in english or had our next languish than convert to see cole. And we all know. Natural language is ambiguous machine languages on bigger after resolve all amputate. He yard a too harsh correctly. Furthermore was different from compared to on other program. Language is the mapping. From adams. To see cole is under specified. If you don't know the schema really depend on what is the structure of schema and so he still model has to really learn how to reason using it. And in order to resolve all that may retail and correctly predicted the sequel and lastly this printer model some. You don't want to just work on this domain one. To work on demand is on databases. You're never seen before. So without st cross domain across database part of it and dodgers very challenging. Guess it's completely different. Distribution wants moved to different dimensions even

Cole Adams Dodgers
The Not so Digital Workforce

Think: Sustainability

02:04 min | Last month

The Not so Digital Workforce

"You may think of the digital workforce as zoom meetings and shed google docs but this trend encompasses a wide range of industries and types of work. This labor refers to a really wide suite of different types of work quite often The moment is being used to refer to digital knowledge. Work so any works. That's that can be undertaken through computers. I virtually remotely roth than having to be in a specific geographical location. That's david vissel. David is a human geography at the university of melbourne and he researches the changing relationship between people and place. There's a wide spectrum of other types of works that could equally be referred to as digital works so the economy in in cities. So things like uber and delivery and all of those new types of services that we're seeing springing up in in our cities that are absolutely reliance on networks of connected mobile phones and algorithms that drive that drive both the workers and consumers so even sectors threats we traditionally associate with being very different and very not digital say things like mining for example are increasingly using. Ai and different types of autonomous developments. So yes a labor certainly a massive consideration through across a lot of different sectors of the moment and it's very variable bull people participating in the digital workforce than ever before this rapid change is something. That's come out of necessity with the emergence of the pandemic but as david explains this influx of flexible and digital workers has an impact on the way how cities function well hit potentially involves all of us in terms of the effects that it has so even if you don't work at all and no doubt you purchase things and you use different online services so even consumers are using dish labor.

David Vissel University Of Melbourne Google David
Seth Dobrin Talks About Trustworthy AI

Eye On A.I.

01:41 min | Last month

Seth Dobrin Talks About Trustworthy AI

"We're gonna talk about trustworthy a i. It's something that is increasingly in the news and concerns a lot of people. Ibm has a product called fact sheets. Three sixty that i understand is going to be integrated into products. Can you tell us what fact sheets three sixty is. And then we'll get into the science behind. Yes so let me start by laying out what we see is the critical components Trustworthy a at a high level Three things there's a ethics there's govern dated ai and then there's an open and diverse ecosystem an ai ethics is fully aligned with with our ethical principles that we've published with arbin dr ceo co leading the initiative out of the world economic forum. And i'm adviser for essentially open sourcing our perspective on a ethics from a govern data in ai perspective. It falls into five buckets. So i is. Transparency second is explain ability third is robustness. Fourth is privacy and fifth is fairness and so the goal of fact sheets is to span multiple of these components and to provide a level of explain ability. That is needed to drive adoption and ultimately for regulatory compliance. And you think of it as a nutritional label for ai where nutritional labels are designed to help us as consumers of prepackaged foods to understand what are the nutritional components of him. What's healthy for us. What's not healthy for us. Factually is designed to provide a similar level capability for a.

Arbin Dr Ceo Co IBM
"ai" Discussed on AI Today Podcast: Artificial Intelligence Insights, Experts, and Opinion

AI Today Podcast: Artificial Intelligence Insights, Experts, and Opinion

04:46 min | Last month

"ai" Discussed on AI Today Podcast: Artificial Intelligence Insights, Experts, and Opinion

"Everybody even has but different people have about about technology right be everybody's people have different levels of fear of technology and the thing about fears that they may be emotional. But there's always something to it right you know. He has unreal as some of these things might be. Some people are afraid that you know artificial general intelligence. Agi if you attended any of our foundations courses this is sort of the strong ai. The idea that we could build this one intelligence system that can do anything that we that we give it to rather than what we have now which are all narrow. Ai which are individualized ai. Thinks that can only do the task that we have specifically train them to do right and only then with a certain level of accuracy. There's no nobody's built sort of general intelligence but there's a fear that somebody will or some organizations will and that system will become really strong and will take over everything right that that's a fear right. The other fear is that robots will take over their jobs. You know whether it's the physical robots and are moving things around in the real world or the software robots that are doing these. You know these tasks machines right. There's their fears about that right. There's also a fear of loss of control. People are afraid that they're gonna lose control over over their privacy over data their decision making you know their right of choice. You know all sorts of stuff people are worried about. They're afraid that algorithms will will make decisions against them and there may be bias or maybe you know some some other aspects of control you know small number of people control over these are these are fierce right and and you can't dismiss these fears because these these fears are there. The fears are real. You know the thing that they might be people might be afraid of. That may or may not be real right now but the fierce themselves are real right. And so we're we're afraid of these things and and so we have to when we when we think about what we're doing. They asked if you have to address the fierce because if you don't address the fierce then even as you might think of them as non rational fears you know. We don't have a super intelligent. So why are you afraid of it right now but the our fears and there there are fears are real and so we have to do some things in our. Ai systems to make sure that people don't have in the back of their mind this concern. You know that that may or may not ever happen right. And of course you have issues of over-concentration of data right so on the flip side. We do have real concerns. In addition to these fears are mostly driven by sort of What people are thinking in their head is as to what may or may not happen. We actually have legitimate concerns about ai. Based on what we have experienced right we have issues of lack of transparency. These are we have machine learning systems that are making decisions and it is true that we in many cases we have a very limited amount of visibility into what's actually happening. Why did that decision happen. You may not have a satisfactory answer right. The other legitimate concern is that while the systems may be sort of neutral. Neither good nor bad. they're just technology..

ai
Interview With Patrick Bangert of Samsung SDS

AI in Business

02:01 min | Last month

Interview With Patrick Bangert of Samsung SDS

"So patrick i'm glad to be able to have you with us on the program here today and we're gonna be talking. Ai at the edge particularly in the world of medical devices. Which is i know where a lot of your focus is here. We're gonna get into some of the unique challenges of leveraging data and ai at the edge in the medical space. But i want to talk first. About what kinds of products. We're talking about people think medical devices. Okay well medtronic is tracking my blood sugar on the side of my arm and you know. Then i've got a big cat scan machine kicking around over here. What kind of devices does your work involve with. And and his edge relevant From your experience. Thank you for having me on the show pleasure to be here. We are dealing with medical imaging devices. So if you have a smart watch on your wrist. That's not what we deal with. Even though those are very useful of course to measure your exercise and sleep patterns we're dealing with technologies like an ultrasound and mri is not an x ray. And what's called digital pathology which is where a biopsy is removed and put on a microscopic slide. Those kinds of technologies produce images that are relevant to telling you whether you're sick at all hopefully not or if you are what kind of disease it is. And so the job of computer vision in this case is to detect whether is a disease diagnose what it is to find out where it is to find out how big it is advanced in if cancer stage one. Three how advanced it is. And all of these outputs can of course be created. Virtually instantaneously by executing artificial intelligence models at the edge and the edge in this case is the device itself. Yeah okay so. Some devices are huge. Mri scanners take up a whole room. As some devices are quite small ultrasound. Machines view could transport it in your suitcase and so there's obviously also price difference here but nonetheless. All of these technologies do produce an image that that is then analyzed by

Medtronic Patrick Cancer
Social Commonsense Reasoning With Yejin Choi

The TWIML AI Podcast

02:07 min | Last month

Social Commonsense Reasoning With Yejin Choi

"All right everyone. I am on the line with jin. Choi eugen is a professor at the university of washington. Yajun welcome to the air podcast and excited to be here. Thanks for having me. I'm really looking forward to digging into our conversation. I'd love to have you start by sharing a little bit about your background and how you came to work in the field of ai. Right so i primarily work in the area of natural language processing but like any other feels of ai. now the boundaries become looser losers and. I'm excited to work on the boundaries between language and vision language and perception and also thinking a lot about the connection between a i and human intelligence and what are the fundamental differences in that in terms of knowledge and reasoning And so let's go a little bit deeper into that. Talk us through like some of the ways that you take on those topics in your research portfolio. What are some of the main projects. You're working on the things that you're exploring right so currently i'm the most excited about the notion of commonsense knowledge and reasoning. This was in fact the only dream of a field. The in seventy eight as people love to think about it and tried to develop formalism for it. It turns out it's really trivial for humans but really difficult even for the smartest people to really think about how to define it formally so that machines can execute it as a program so for a long time. Scientists assumed that it's Doomed the direction. Because it's just too hard so i didn't really thought about commonsense for for a long time and then it's only in recent years. Some of us got excited to think about it again. Which is in part powered by the recent advancements of neural modell's that is able to understand large amount of data.

Choi Eugen Yajun JIN University Of Washington
Microsoft Teams Is Getting Hybrid Meeting Features, Including CarPlay Support

Daily Tech News Show

00:25 sec | Last month

Microsoft Teams Is Getting Hybrid Meeting Features, Including CarPlay Support

"Announced several updates for teams including audio only support for apple. Carplay is also a cameo feature coming to powerpoint live to insert teams camera into a slide deck slide and ai powered speaker coach. Coming in twenty twenty two. That will offer speaking tips and automatic correction tools for video in the coming months. Teams will also add support for intelligent cameras from. Oem's like jabra neat polly and ye link able to track speakers during a

Carplay Apple OEM
Apple Watch Executive Takes Over Secretive Car Project

Geek News Central

01:09 min | Last month

Apple Watch Executive Takes Over Secretive Car Project

"Apples always pushing the edge. And i thought apple kind of watched walked away from from cars but the apple watch executive is taking over secretive car project now. Just two days before hideaway. Doug field ahead of apple secretive car project that tech china's tap apple watch exact former adobe. Co kevin lynch to take his place so in the latest changing of the guard for the project known as project titan which is rotated leaders about as much as reporting shifted focused They replace individual so he's been working on this since july when he was brought in to help develop the vehicle software. He's been with apple since two thousand thirteen. Curiously bloomberg rights at lynch still reports to apple's cheap chief operating officer jeff williams and not to john daria the company's head of ai. So we'll keep a watch on what's apple doing anything apple build some apple bands will

Apple Doug Field Kevin Lynch Adobe China John Daria Bloomberg Jeff Williams Lynch
Deep Reinforcement Learning for Game Testing at EA With Konrad Tollmar

The TWIML AI Podcast

01:48 min | Last month

Deep Reinforcement Learning for Game Testing at EA With Konrad Tollmar

"Conrad woke him to the tuomo. Podcasts thanks sam. Thanks for inviting us to be here. I'm really looking forward to digging into our conversation. We'll be talking about As the audience might imagine the intersection of and games before we do. I'd love to have you share a little bit about your background. I mentioned what is k t h. Okay teaches royal institute of technology in stockholm. It's a technical university where i did my undergraduate as well as might be hd. So i i think my interest for a i started longtime ago starting with computer vision. I always been passionate about photography. And i saw them. There was an opportunity to combine my kind of interest for photography than webs kind of my academic. And the so. That's kind of my starting point here. Nice and tell us a little bit about the kind of research that interests you in your professorship and on your graduate studies so my phd more symbolic media spaces and we build different kinds of interactive in viramontes to connect places with vdi streams but also being able to use sensors to convey other kinds of information. If you're close or if you're in the proximity of a space for that led me and benchley to explore that further or after my ideas and i spent some time working smart and interactive environments some over this work for play and some were for more like everyday use and i think some of us could remember recall. The kind of demos sue sorted out the mit's media on the late nineties.

Royal Institute Of Technology Conrad SAM Stockholm Benchley MIT
Ethics Panels Reject and Delay Biometrics, AI Projects for Google, IBM, Microsoft

Daily Tech Headlines

00:29 sec | Last month

Ethics Panels Reject and Delay Biometrics, AI Projects for Google, IBM, Microsoft

"With ai. Ethics chiefs at google microsoft and ibm published by reuters looks at what. Ai projects these companies reject in september. Twenty twenty google's cloud unit rejected a financial firm looking to us to determine credit worthiness and earlier this year. Google blocked features from analyzing emotions. Ibm turned down a client request for a more advanced facial recognition system microsoft place limits on software mimicking voices. All three companies said they welcomed clear regulation for

Google IBM AI Reuters Microsoft
The Future of Direct Response Marketing With Internet Marketing Pioneer Rich Schefren

Uncensored Direct Marketing

01:59 min | Last month

The Future of Direct Response Marketing With Internet Marketing Pioneer Rich Schefren

"What do you see in the next ten years in direct response. We're going now. You know in my opinion. Just from what i'm seeing from the market as we speak like a more individualized approach for sure in terms of selling. You know obviously the changes with you know targeting and so forth with apple and so forth where you seeing like direct response going in the next ten years. Okay will some of the obvious stuff right like which like i i'll spend a few minutes. Everyone focused on stuff. That is less obvious So obviously data's going to be huge right. And i think that like some of those things that i did from two thousand seventeen to two thousand nine hundred ninety gora before we launched my thing. Right was I got gave the opportunity to look at enterprise level Ai tools that we haven't seen anything like in our world yet right And the which made me very aware very early on. Just how much like a is completely worthless about any data. And it's marginally better if it's someone else's data what really makes it valuable as your data about your customers and what they're doing right and unfortunately most entrepreneurs are not even capturing the first party data that they're entitled to for example like most people don't download all of their information from their ad accounts. And if you lose your account you lose all that data and that's gone forever goodbye right so you know first party. Data is going to be increasingly more important. That's one of the reasons why. I'm going down the road that i'm going. I need to know all that information myself about my customers. What their preferences are what. They're looking at. What turns them on what turns them off. I can't rely on a platform nor is it wise to rely on a platform. I mean the platforms are really out for themselves at the end of the

Apple
How Technological Advancements Are Changing Consumer Research With MediaScience CEO Dr. Duane Varan

MarTech Podcast

02:00 min | Last month

How Technological Advancements Are Changing Consumer Research With MediaScience CEO Dr. Duane Varan

"Dr duane walk the martic tech podcast. It's bigger thanks for having me excited heavy. As our cast excited to talk a little you know of the more technical side of marketing. This is the mark tech podcast. Normally kind of focus on the mar part. And you're going to bring some tech influence here in the sense of machine. Learning artificial intelligence the more sophisticated technologies we use. Let's start off talking a little bit about media. Science in the description of this podcast. I mentioned biometrics facial expression i tracking. Eg g. those sound like really complicated technologies. how are they actually being used in marketing. I mean they are complicated. Of course the issue that we address in our research is that when you're talking about marketing above all your taxes back human emotion but the tools that we use to get to human emotion usually depend on self report in other words whether it's a focus group for a survey or interview were relying on woke people. Tell us about their most journey. The problem is people lack an understanding of own motionless journey. So when you ask a person a question about how. They feel about something what they're giving. You is the rash on reputation of what they think. They must be feeling. And that's actually far removed from their actual emotional encounter so what we do at media. Science is we want to measure that emotional response directly rather than being just depended upon what people tell about it. So the tools that you mentioned are all tools that are designed to get at measuring that emotion directly. I mean they are fairly complex. One of the reason they're complex is because they very pressing the person so you can't do this against a generic set of measures you have to actually calibrate for the individual. And then you have to actually let the that individual's response relative to their universe so to speak so that you can situate them in terms of what it means for them against the data but very exciting because it just exposes layers of data that we don't see otherwise

Dr Duane
A Powerful Intersection of AI and Robotic Process Automation With Merve Unuvar

AI in Business

01:54 min | Last month

A Powerful Intersection of AI and Robotic Process Automation With Merve Unuvar

"So marvan. I'm glad to have you here with us on the show and i know we're diving into the topic of rpa intersection with a i. I think given the coverted era is a lot of thinking about gaining efficiencies about finding opportunities for automation when you're working with big enterprises obviously. Ibm works as many of the largest firms in the world. How do you walk people through finding those pockets where automation could make a difference. What does it look like spot opportunities in workflows yet. Thank you that. This is a very interesting area. Especially as he emphasized during this pandemic a company has realized that some of the workflows could be rethought through given most of their workforce with two remote working right so before we discussed this topic further. I'd like to open up the definitions of key concepts here for the audience. So what is it business workflow. It's basically an execution of business processes that contain tasks information and paperwork related to all of these right and then they're passed from one person to another to achieve a business school better. It could be alone operable for a bank or could be a claim submissions furnishings company. So this usually moms one or more people and a hub can best leverage automation in these workflows needs to be thought through in a few dimensions so the first one is from overall process and the workflow performance point of view so in order to analyze the performance. Right manner i. We need to understand the end goal of workflow if he thinks through the same mortgage scenario is the goal to sell more loans or is it to process loans faster or it can be combination of these metrics but we need to really define the key performance indicator or the goal of these workflows and then start monitoring the performance towards these goals and one of the very obvious waste of flying. The pockets of automation is then to find the bottleneck tasks in these workflows that will impact the

Marvan IBM
Jaron Lanier on the Future of Humans and AI

Lex Fridman Podcast

02:18 min | Last month

Jaron Lanier on the Future of Humans and AI

"You're considered the founding father of virtual reality. Do you think we will one day. Spend most or all of our lives in virtual reality worlds. I have always found the very most valuable moment in virtual reality to be the moment. When you take off the headset and your senses are refreshed and you perceive physicality afresh. You know as if you were newborn baby. But with a little more experienced he can really notice just how incredibly strange in delicate and julia impossible. The real world is Sue the magic is and perhaps forever will be in the physical world. Well that's my take on it. That's just me. I mean. I think i don't get to tell everybody else how to think or how to experience retreat. At this point there have been multiple generations of younger people who've come along and liberated me from having to worry about these things But i should say also even in a what. I called it mixed reality back in the day in these days. It's called augmented reality But with something like a hall and even then like one of my favorite things to augment a forest. Not because i think the forest needs augmentation but when you look at the augmentation next to a real tree the real tree just pops out as being astounding you know it's it's interactive. It's changing slightly all the time if you pay attention and it's hard to pay attention to that but when you compare to reality all of a sudden you do and even in practical applications My my favorite early application of retrea audi which we prototype going back to the eighties. When i was working with dr joe rosa and at stanford med near near where we are now. We made the first surgical simulator and to go from the fake anatomy of the simulation which is incredibly valuable for many things for designing procedures for training things then to go to the real person. Boy it's really something like Surgeons really get woken up by the transition. It's very cool. So i think the transition is actually more valuable than the simulation

Julia Dr Joe Rosa Audi Stanford
"ai" Discussed on The TWIML AI Podcast

The TWIML AI Podcast

04:29 min | Last month

"ai" Discussed on The TWIML AI Podcast

"To the tuomo podcasts. I'm your host sam charrington. Thanks so much for joining us. And if this is your first time i invite you to. Hit subscribe and apple podcasts. Spotify youtube or wherever else. You might be listening to the show all right everyone. I am here with kaifu. Lee chi food is chairman and ceo of innovation ventures the former president of google china and author of the new york times bestseller superpowers. And we're here to talk about his new book which will be released next week. A twenty forty one kaifu. Welcome to the tuomo. Ai podcast thank you thank them. It is great to have an opportunity to speak with you. I'm looking forward to digging in and talking more about the book before we do though i'd love to have you share a little bit about your background and how you came to work in the field of ai. Sure i started With my excitement in back in nineteen seventy nine. When i started my undergraduate at columbia i worked on language and vision at columbia and then i went to carnegie mellon for my team at which develops the first speaker independent speech. Recognition system based on machine learning actually Some the earlier thesis in machine learning in nineteen aba. I also developed a computer program that the world's fellow champion is all in the eighties. Very early years after mike graduation from Cmu i talked there for two years than i joined apple and led a a lot of apples. Ai speech natural language and media efforts later joined sgi and then microsoft where i started microsoft research asia in beijing in nineteen ninety eight which kind of became one of the best. Tom research labs in asia. Later i joined google and ran google china for four years between two thousand and five in two thousand nine. We did do a little bit for how they i mostly was Really developing google's presence in china in two thousand nine. I left google and started my venture capital firm assign ovation ventures and at san ovation ventures we invest in the bow for the ai companies. We were about the earliest and probably invested in the most companies we invested in about seven unicorns in ai alone and with a few more Yet to come so they're excited to be in the era i it's Was not so hot during much of my career. But glad scooby with the catch. The recent wave and participate in it. Fantastic fantastic so. Let's maybe jump into the book. The title is ai. Twenty forty one. She just read. That heard nothing of the book. You might think that is kind of a straight up beer visit for a in twenty forty one but and and to some degree. That is the case. You're asking interesting questions on that time horizon but there was a little bit of a twist. Tell us about that. Twist and the way. The book is organized shore. The twist is I call this book scientific fiction. Because i collaborated with a science fiction writer who wrote most of the book probably three quarters as they are ten stories call ten visions of the future. I find that of the impact of. Ai is misunderstood by a lot of people. some are too conservative. others are too optimistic Others are just naive and And some explanation. I think it'd be helpful. Ai will change our future and more people need to understand it and having a a fictional writer right in in terms of stories will make it all the more accessible to people so. The book is organized intense stories. Each of which is Told in the different each of which takes place in a different country and in the different industry so we can see how a i will impact all countries in all industries. And then after each story i write an analysis of the technologies embedded in the.

kaifu sam charrington Lee chi innovation ventures google carnegie mellon china Tom research labs columbia apple san ovation ventures asia microsoft new york times sgi youtube beijing mike Ai
"ai" Discussed on The TWIML AI Podcast

The TWIML AI Podcast

02:31 min | Last month

"ai" Discussed on The TWIML AI Podcast

"All right everyone. I am here with kaifu. Lee chi food is chairman and ceo of innovation ventures the former president of google china and author of the new york times bestseller superpowers. And we're here to talk about his new book which will be released next week. A twenty forty one kaifu. Welcome to the tuomo. Ai podcast thank you thank them. It is great to have an opportunity to speak with you. I'm looking forward to digging in and talking more about the book before we do though i'd love to have you share a little bit about your background and how you came to work in the field of ai. Sure i started With my excitement in back in nineteen seventy nine. When i started my undergraduate at columbia i worked on language and vision at columbia and then i went to carnegie mellon for my team at which develops the first speaker independent speech. Recognition system based on machine learning actually Some the earlier thesis in machine learning in nineteen aba. I also developed a computer program that the world's fellow champion is all in the eighties. Very early years after mike graduation from Cmu i talked there for two years than i joined apple and led a a lot of apples. Ai speech natural language and media efforts later joined sgi and then microsoft where i started microsoft research asia in beijing in nineteen ninety eight which kind of became one of the best. Tom research labs in asia. Later i joined google and ran google china for four years between two thousand and five in two thousand nine. We did do a little bit for how they i mostly was Really developing google's presence in china in two thousand nine. I left google and started my venture capital firm assign ovation ventures and at san ovation ventures we invest in the bow for the ai companies. We were about the earliest and probably invested in the most companies we invested in about seven unicorns in ai alone and with a few more Yet to come so they're excited to be in the era i it's Was not so hot during much of my career. But glad scooby with the catch. The recent wave and participate in it.

kaifu sam charrington Lee chi innovation ventures google carnegie mellon china Tom research labs columbia apple san ovation ventures asia microsoft new york times sgi youtube beijing mike Ai
Exploring AI With Kai-Fu Lee

The TWIML AI Podcast

02:31 min | Last month

Exploring AI With Kai-Fu Lee

"All right everyone. I am here with kaifu. Lee chi food is chairman and ceo of innovation ventures the former president of google china and author of the new york times bestseller superpowers. And we're here to talk about his new book which will be released next week. A twenty forty one kaifu. Welcome to the tuomo. Ai podcast thank you thank them. It is great to have an opportunity to speak with you. I'm looking forward to digging in and talking more about the book before we do though i'd love to have you share a little bit about your background and how you came to work in the field of ai. Sure i started With my excitement in back in nineteen seventy nine. When i started my undergraduate at columbia i worked on language and vision at columbia and then i went to carnegie mellon for my team at which develops the first speaker independent speech. Recognition system based on machine learning actually Some the earlier thesis in machine learning in nineteen aba. I also developed a computer program that the world's fellow champion is all in the eighties. Very early years after mike graduation from Cmu i talked there for two years than i joined apple and led a a lot of apples. Ai speech natural language and media efforts later joined sgi and then microsoft where i started microsoft research asia in beijing in nineteen ninety eight which kind of became one of the best. Tom research labs in asia. Later i joined google and ran google china for four years between two thousand and five in two thousand nine. We did do a little bit for how they i mostly was Really developing google's presence in china in two thousand nine. I left google and started my venture capital firm assign ovation ventures and at san ovation ventures we invest in the bow for the ai companies. We were about the earliest and probably invested in the most companies we invested in about seven unicorns in ai alone and with a few more Yet to come so they're excited to be in the era i it's Was not so hot during much of my career. But glad scooby with the catch. The recent wave and participate in it.

Kaifu Lee Chi Innovation Ventures CMU Google Columbia China New York Times Tom Research Labs Microsoft Asia SGI San Ovation Ventures Mike Beijing Apple
VW Debuts the ID Buzz Autonomous Van

Daily Tech Headlines

00:19 sec | Last month

VW Debuts the ID Buzz Autonomous Van

"Volkswagen debuted its. Id buzz autonomous e van. The twenty twenty one. I a mobility event in munich. This features autonomous systems developed by argot. Ai vw plans to use. Id buzz as the platform for its full-scale commercial. Ride hailing and delivery operations plans launch germany. In twenty twenty-five

Volkswagen Munich Germany
"ai" Discussed on AI in Financial Services Podcast

AI in Financial Services Podcast

05:43 min | Last month

"ai" Discussed on AI in Financial Services Podcast

"Step of the process. You wanna ask permission to the owner to be adding new people to that particular project and only dan. Those guys will be able to see the dea wore these extra governance measures in regulated industries. It seems like it's another layer that honestly it kind of makes it hard even to assess maturity right because you can't necessarily get a hold of the state of their data on how harmonized as soon as they ask you a question. There's all these layers to go through. It does feel like that would make things a little bit tougher for you. Yup yeah i mean. But at the end of the day. I'm a big proponent proponent of responsible way i especially when you're dealing with sensitive information of course so you know having number one owner of the data right now is the organizations not necessarily the the the customer but having the customer gift consent to the organization so they can actually use the data in the ai systems to me. It's also part of a immaturity. Some organizations may just be using the data on the sally asking individuals consent. And to me. I think that is not gonna go very far because we know that as far as The financial industry's concern the federal reserve. Right now is working on a request for information where they're starting to to understand how those regulated institutions are using individuals data. And they're gonna come up with regulations around that so if you're not ready to provide a framework where you can actually control who see the data in win. Most likely the federal reserve will will not be okay with that so frail issues with that coming. Come for yeah for sure and clearly. I mean we've talked to certainly on public. Podcast would advocate for reckless use of customers data in banking context of you know very predictable response in my perspective here but when it comes to we can see in the banking space. We actually see a lot of spend when it comes to. Ai being applied directly to those regulatory matters in other words how can we. How can we better respond to the regulators. How can we make sure that we're being transparent. We are seeing a lot of attention being paid to those factors before we wrap up on this topic of maturity. I did wanna get very quickly through as a a quick last blitz. You have five questions you like to ask around how ready team in a pod might be to actually move forward with the project you've seen dozens and dozens and dozens of pilots. Come across your desk. And then you've seen many more of them actually roll out into actual departments and you had to get good at filtering stuff. You have five questions like to use when you're deciding on those sorts of projects. Can you walk through those really quickly just so that some of our listeners at home might be able to use these themselves. Yeah for sure. So you know a lot of A lot of people are very curious about machine learning and may have valid use gays. But they don't know yet so sometimes people may be able to solve the problems trying to solve using other tools than unnecessarily et a machine learning so the first thing i ask is. What is the problem. You're trying to solve. I so what i tell them is if you had a crystal ball that you could make any predictions. How will you then use those predictions in the front lines..

federal reserve dea dan
"ai" Discussed on AI in Financial Services Podcast

AI in Financial Services Podcast

01:44 min | Last month

"ai" Discussed on AI in Financial Services Podcast

"So ilan today we're talking about maturity and readiness. I appreciate you as a emerge a subscriber with us here but also is somebody with some very unique experience working in one of the largest companies in america actually bringing ai to life. Let us know how you think about this topic of maturity and kind of how you assess it. Hi dan thank you so much for inviting me. It's been an honor to be here with you or yup. Yeah so my job is very unique in the sense that i'm not necessarily evita. Science is my job is to understand. The needs of big enterprises would save fifty to half a million employees and how they can best utilize machine learning and opie computer vision in their lines of business yet. Some of this enterprises can have a hundred lines of businesses. Each one of them will be in different levels of ai maturity yes so my job is to come in and understand what problems of trying to solve and how they i can actually solve that up so in order to do that. I have actually leveraged the work that you guys have done at emerge. I learned from you. Guys in to assess the level of maturity from different lines of businesses mainly mainly three components. That luke for one is the skills are available in that line of business to is the coach or the business culture of the company out of business as well as the importance of data. So let's deal to each one of those in terms of skills. It's very important that there are unavailable skills of course but also it's also important to understand that. Ai is a team sport.

ilana glazer synergy ai Ilan alon wells fargo bay area ilan google
"ai" Discussed on AI in Financial Services Podcast

AI in Financial Services Podcast

02:35 min | Last month

"ai" Discussed on AI in Financial Services Podcast

"Podcast it's great when we work with large organizations here emerge and we're able to see some of our work come to life in terms of in terms of how enterprise getting more value out of their ai at deployments and it's also cool to see how some of our emerge plus frameworks and use cases are helping individual professionals apply these concepts inside of large companies. So sometimes we're able to work with companies sometimes we offer Solutions frameworks that that help people in large companies and our guest this week in his example of someone who's put emerged concepts as well as their own hard lessons learned to good use our guest. This week is ilana glazer. He is the founder and ceo of synergy ai which is an a consultancy based in the bay area. He's had a lot of hands on experience with very large enterprises including some work as a product owner for ai. Enterprise solutions at wells fargo. Which is one of the largest financial services in the world So alon has a lot of great experience. He's being interviewed today as again founder and ceo of synergy and he speaks to us about the topic of maturity when we're looking at an individual line of business and we're figuring out these people ready to leverage ai or their specific questions we should be asking and there's factors we should be considering and those of you who listen in. On this week's episode and how ilan describes a immaturity you will recognize emerges critical capability model. If you go on google and you type in e. m. rj and then you type in critical capabilities. You'll find our model for a maturity which is a three part model. Ilan talks about some of those concepts in greater depth in terms of his own experience using them and then also talk a little bit about how to actually assess them. How do we assess the skills the culture and the actual resources data of a specific line of business. What are the kinds of questions. What are the kinds of of investigations. We need to do to figure out. Is this line of businesses. This group of folks within this large enterprise actually ready to consider using ai. In the first place at the end of this episode. Ilan talks about his five questions. These are tools that he uses when he's working with a team. That's ready to move forward on a and now it's time to pick their projects. How do we assess each individual project. The questions we asked to make sure this is actually a realistic project. What's a a short and fast way to be able to sum up what we think. The impact will be an determine if it's viable or not well lot. I think has a useful tool in terms of these five questions that he uses. And i hope that they'll come in handy for some of you. Who are tuned in again. Whether you work with in a large enterprise or you work with large enterprises. Ilan hands on experience. I hope is going to end up. Being valuable. alon is one of our emerge subscribers..

ilana glazer synergy ai Ilan alon wells fargo bay area ilan google
"ai" Discussed on Practical AI: Machine Learning & Data Science

Practical AI: Machine Learning & Data Science

07:28 min | 2 months ago

"ai" Discussed on Practical AI: Machine Learning & Data Science

"Just getting into some of this discussion about how is represented in the cantonese context and starting to talk about how there is embedded within the term this idea of manual labor or something very yes. One of the things that mentioned in this article is thinking about you. Know what if these systems what if ai systems or automated systems are augmented systems. What if these systems were seen less exceptional or very sophisticated or whatever that term is and more ordinary or less exceptional. You don't wanna take me down this path strong opinions on this. You don't do that to you. Yeah so there's like the things that he draws out are more ordinary and unless exceptional or more human and less machine more labor in less enchantment. I thought that was a cool like a pairing of terms so like there is this perception. Maybe not by the practitioner. Like you were talking about but definitely by people at large the ai systems. Have some sort of enchantment around him. When i'm teaching classes. I think oftentimes when i get into like okay while if we think about most ai systems and there are certainly exceptions. That don't fit into this pattern than i am about state but if we think about most ai systems what we're talking about is essentially an automated process of trial and error. It's actually a very dumb process right in the sense that we have a set of examples were trying to recreate the data transformation from that input to output. And the way in which we do it. Is we try a bunch of times so we examples and we try to make the prediction based on some encoded parameters. And when it's not good we changed the parameters and then we just do that a jillion times until we get the right set or the quote best set of parameters and then outcomes the model which so many people see as enchantment or put fairy dust on the computer in the corner and you know i see some faces in training. Sometimes where it's like. Oh it's a little bit disappointing for people in other ways maybe reassuring because they came into it thinking it's harder to get into this practice than they thought because there's some like fundamentally subtle and mysterious thing that they need to understand in order to do the work daniel the here. It's a black box. It's a black box. it's magical. This is their way into hogwarts. There's another term black box right. It is what does that terminology imply about how we think about at least. I don't know how people are thinking about it now but earlier. it was it's unknowable. And that was really before the rise of explainable as a field. Yeah i mean that's what blackbox was intended to be. Is this a noble what we're here. Which of course is a little bit silly unfortunate connotation with black just meaning. It does have a negative connotation in the context of like most of the times these terms that people have used through. Time like a blacklist or something which now are sort of trying to get away from those terms because we definitely don't want that connotation in many cases which like black shouldn't have this sort of pre existing negative connotation and so when you say blackbox there's like this part of part of that term is like this unknowing nece about it but from the start at least in our culture you also get the sense of. Maybe you're trying to say something something. Negative about the ai. Thing that it's blackbox it's unknowing but maybe an unknowing threatening sort of way. There has always been kind of a mysticism around the term artificial intelligence or ai and frankly there have been many marketers out there. You've taken great advantage of that. But what we do. The deep learning techniques that we do is just mathematics and it's very very pragmatic it's very down to earth and there's nothing mystical about it so i think that's one of the places i've arrived when we started the show and named our show practically i. I was probably pretty high on the term. Ai then but as we have really delved into this and the black box. Nature of the math has changed. It's hard for me to think of it. As i certainly don't think of his intelligent. It's hard for me to even think of it as ai. At this point. And so. I think my term. Ai has gotten more aspirational as we've gone on meaning that we have not yet achieved it. Yeah it's this system or machinery like was talking about in that sort of breakdown of the cantonese the system or machinery. That is doing some kind of man or woman related power labor. And so yeah. I think that's a very practical way to look at it. And i know in the past you know just speaking of lexicon and how talk about ai. Even this recent episode where we chatted with the people from sly so meghan naked. Ernie mentioned i. I was watching that. This sort of premise of that show is their concepts around chopped. The cooking show. And i found it very interesting that that's also in my lexicon of ai terms. That's very much how i think about actual ai development and the practicalities of ai development is much more like cooking than it. Is this sort of like pure research topic of people standing at a chalkboard and doing various things and so that in the spirit of what. Ai now is trying to do. You know i would think that. For example like a recipe is a good term to have in a lexicon where i agree. It's not because even like if you think about a model model has certain connotations especially for people aren't like deeply rooted in maybe a scientific discipline like physics or chemistry or mathematical simulations. They may think that that model term is also quite intimidating but This is another. Maybe earth-shattering thing for people. When when i'm doing trainings as a model. What is a model while. Maybe chris if someone was to ask you. What is an ai model at. Its core like when you get your magnifying glass and you look at the i. Model would it is composed of i. Guess maybe there's another way will the composed off threw me off a little bit. I think of a model in a in generically in this isn't specific necessarily too deep learning but as a representation of an entity that is real in some sense that doesn't mean necessarily as physical it represents the characteristics and stuff but really i mean when we talk about a model in the context of deep learning and were producing a model at the end. We love to talk about the word inference and such but i think of it as a filter honestly yeah type of filter and so you give it an input and it is arrived as a set function that produces an output based on that input. And until you change the model with further training it's filter that gives you a particular set of outputs and.

daniel meghan ai Ernie chris
"ai" Discussed on Practical AI: Machine Learning & Data Science

Practical AI: Machine Learning & Data Science

07:49 min | 2 months ago

"ai" Discussed on Practical AI: Machine Learning & Data Science

"The series of articles that the Ai now is putting together. A which is a series of blog posts called a new ai lexicon and they talk about. Hey i is having this sort of incursion into all bits of our life and they talk about needing to generate narratives that can offer both perspectives from other places but also crucial anticipatory knowledge and strategy. So in my mind people might not be familiar with the term lexicon. At least before. I worked for an organization that did a bunch of language stuff. It wasn't a term that used very very much. But i'm just looking at the definition here in a lexicon is the vocabulary of a person language or branch of knowledge so i think when they're talking about a new lexicon a i would maybe fit into this branch of knowledge category and we're talking about the vocabulary of this branch of knowledge for the moment you know for the moment. Yeah and i think one of the hardest things for me. When i was getting into this field was the jargon and vocabulary because even from technical background i had run across certain things like you know whatever. It is ordinarily squares or something. But the way the jargon in which ai people are data. Science people talked about these things with different vocabulary jargon so it wasn't so much like the thing was scary or complicated. It was the vocabulary actually made it scary and complicated. What what's your experience there. I agree with you. 'cause language is powerful and how we label. Things is powerful. I have strong opinions air. We get into his four. Even in the time that we've been doing this show for those who might have been listening to us the early days it's evolved quite a bit and so i think this is a timely topic. Personally yeah for sure. Maybe i'll just illustrate what i'm talking about. One of these articles. Which i think is a good starting point for this discussion. It's the article called an electronic brain naming categorizing. The futures of i the guest post by a doctoral researcher at oxford young. How certify mispronounce your name but this article. I thought was just fascinating. I did too. I think all of these articles. If i was to put them in a category would be like thought provoking or something. Because i don't know you know reading through the bulk of them. I don't think maybe anyone could read through all of them and maybe say hey. I fully agree with everything that's written here. That's my perspective too. It's different perspectives on the subject. Of a i so. I think it's like thought provoking it helps you sort of reformulate how you think about a and the language that you use around ai and in this article the electronic brain young the author talks about cantonese terms and the way that cantonese terms are are made up is that they're composed of a set of characters and each individual character and its components. Have some sort of meaning and so what. The author did was look through various terms for things related to a select computer artificial intelligence automation etc and looked at these components in the cantonese language. For sort of insights. Maybe about how these terms from the cantonese perspective might sorta shed light on how we think about a related things and by the way just as a two second aside for anybody that has not familiar with cantonese out there. It is the second most dominant language in china i of its usage. It lags far behind mandarin at this point because that is the communist. Party's official language but can't knees would be the second most widely spoken language in china. I language related things. Just the plug if you if you're wanting some knowledge about like languages and their relationships and populations in all of that you can look at the log as one good place to look for that information but there's also sort of open versions that are sort of. You'll see these trees of language varieties and how many people speak them populations and all that stuff. The author goes through an breaks apart. Some of these common term so for example for computer this sort of breakdown of that in terms of the characters that he brought out was called electric brain. Which i don't know when you look at a computer. What is your. are you thinking. Electric brain indeed. I do not in what way. I mean the brain heart. I mean it's definitely electric right. It is electric. I don't doubt that and our brains or electric as well. So maybe your electric brain is redundant because brain implies electric actually. That's not where my head was at. That is in fact true. They both have electrical signals in them. That are core to their functionality But i think that's about the limit of my Analogy there yeah. So that's computer. Which is i think Pretty interesting he gives sort of alternatives and breaks down the individual characters. I showed one of my colleagues the breakdown and he was seeing. That computer could also maybe be like what he say like meat rain or something which. I don't know what that tells us about how we think about computers. But he goes in and he talks about you know these components also of how we frame the term artificial intelligence which i think is very thought provoking for me and he draws out this idea that the term itself in cantonese the way. It's made up talks about you could think about it primarily as artificial intelligent but also a sort of parallel to that that's in the language is is really more focused on like manual labor or manpower and like able or assists domer- smart able to do manpower or manual labor which is much more of a sort of practical idea about artificial intelligence. How does that strike you. It feels more like the feelings that we have and we say machine learning instead of artificial intelligence. You know the way they do that i think. Practitioners tend to think of machine learning as the pragmatic fundamental. You know stick with the science kind of mentality whereas ai is a little bit more glossy marketing. Though you're speaking of the translation it feels like that's how i would think of as machine learning in my own context signal wire is real time videotheque to help you create interactive video experiences previously. Not possible. He gives you access to broadcast quality. Ultra low latency video. That's proven and trusted by amazon ring doorbell. Zoom and others see why the future of video communication is being built on signal. Wire they have easy to employ. Api's sdk's for the most popular programming languages and expert support from the. Oh jeez of offered fine telecom tech try it today at signal wire dot com and use code a. i. For twenty five dollars in developed credit just visit signal wire dot com. that's signal wire dot com and use code. ai to receive that.

oxford young china amazon ai
"ai" Discussed on AI in Business

AI in Business

04:47 min | 2 months ago

"ai" Discussed on AI in Business

"We can call it a false negative in weekend. Label that feed that back into the system and hopefully overtime micro calibrate micro calibrate micra. Calvary keep us system breathing living accurate. If feels like in this space. We'd have to do something similar. Are you guys working mostly with sas companies or is it kind of various and sundry in terms of what people are selling it or go broader and we're starting to to break out into a pretty wide on the on the category spectrum sas companies has been court area yummy you our core research team and taggers but one of the key aspects of the. I is that leaders. Sales leaders will come in and coach reps. And they'll go down to the specific moments on a scorecard and that's continuing to train our ai. As well as we're getting these moments of what is best from even the wieder say. Hey great job on this specific point and it's just helping kind of highway. What is working and sharing that across the team as well. That feels a little bit tougher so being able to have your own squad of taggers say yes. This was actually pricing. Being talked about in this time this actually was whatever some legal some legal hubbub issue that we were stuck up on for these ten minutes. That that feels you know. You gotta do some of that. Because i don't think your sales reps are doing that. It's hard enough to get them to put their notes into salesforce. Where we're not gonna have tagging audio files so you gotta have that process but the ability for sales manager to highlight things and have that actually feed a system in in a positive. Luke feels a little tougher or we suspect that like i. I would suspect if we're gonna have a systematic effort to say what our winners doing. Let's say i'm i work at spunk. Right some big sass offerings somewhere. I work at slump. And we figure out our top sellers into big enterprise accounts and we figure out we train. On a whole bunch of instances and a whole bunch of sales whatever we have patterns of them and then patterns of the rest of our sales people it would feel like a pretty bespoke effort between you and between the spunk sales leadership to really needy greedy. Dial in those features determine what we want to track really come up with conclusions more so than them just tagging stuff. That's good and then having the system actually tells them that that's good. That feels really tough. You can do about to. Individual teams can set up their own topics that they want to be tracking and identifying those terms then starting to look at whether those are competitors or certain types of features Other aspects that they want to specifically do now would unique is we have a predefined list of these business sales terms that we think everybody looks at so predefined. Plus what you need to you. And then you're right having the data in the dashboards assay tell us what what drives success. I think that's the key thing is based on all the data. We're collecting now. Help us understand what is best and then how do we share that with the rest of the team. He and it feels like to really get to answer there. We're not gonna again have a dashboard. That just shows it. We're going have to think about what we want to call best right. It might not be self evident and then and then probably they'd have to chat with you a little bit and kind of determine what we want to dig out what we want to track..

sas salesforce Luke
"ai" Discussed on AI in Business

AI in Business

05:45 min | 2 months ago

"ai" Discussed on AI in Business

"We have a learning bosmo moses. We have an amount of on boarding process sort of tutelage that occurs in the beginning. Now we're we're wondering all right well. Where does artificial intelligence fit into all of our various sales data in order to potentially layer value onto what on boarding should look like in the future where it begin to fit in. What are the parts not. Everything's getting revolutionized but some bits are. What are those bits. It's interesting what we're seeing. Now is the ability to record and be an all these calls just given where technology as a hook into calendars and plug into zoom. We now the ability to capture these interactions. At a way that we had done before. We're actually in is interactions and conversations. But that's a lot of video content to go through if you were to go through that in real time it. It's not something that the average new hire or manager qaddus were. Ai plays a really critical role. Is identifying those very unique moments. Those moments that drive outcomes the ability to understand what does best next step. Oh quite or how should we asked discovery questions. Or what sales methodology are we following in. How should i. He handle each of the different sections of or an objection the ability to go through the new hire and say i want to hear all the pricing objections. Let me listen to the last. Ten calls were price objection. Came up here. Our best reps answered it and just literally listen to that thirty second piece jumping in jumping out quick comment that ability to quickly here so many of these edge cases dramatically impacts voted on poor people faster. Eso and i can imagine if. I'm a sales manager. If i get a sense of new rep. Extra new rep y where they're getting hung up or where i feel like they're getting hung up. I might ask them to learn more about that part of the process right. Listen to how. Susan handles pricing questions. You know listen to how. Jim opens a call and and really bonds with people quickly. You know that kind of thing right now some of that just grabbing some recordings from jim doesn't necessarily require ai but clearly is being used here somewhere. What you're saying makes me think that. Nlp is able to extract from minute would say fifteen fifty to fifteen minutes. Fifty two seconds to you know Eighteen thirty is a pricing with confidence level of eighty five. We think that this audio clip is is pricing. That's literally what happened in my head when you said that but it probably doesn't work exactly like that. How does it work. But that's smart. We have our team of researchers yarn a phd on our research team as well but we've deeply studied the language of sales and all the different type of language that drives positive outcomes so pricing and the product mentions and objection handling. These are terms that we've studied and we built these topics into our ability to track those moments. We will pre identify. Hey that's an at risk deal. We've identified at risk language. Let's flag it. Let's call it out. Click of a button. Go right to that moment or several moments in a call so these pre identified list of topics that you want to drill into is really the special sauce dot it okay. So tell me if. I'm right or wrong here where is fitting in is. Were recording all the calls. We're leveraging nlp on top of it and we're able to determine when to certain topics come up. Maybe even a little bit about sentiment. I'm not sure and then some kind of flags for at risk deals in my mind. I'd love to know what the lingo is at risk deals..

Ai Susan Jim jim
"ai" Discussed on AI in Business

AI in Business

05:54 min | 2 months ago

"ai" Discussed on AI in Business

"Intelligence research. And you're listening to the ai and business podcast today. We're gonna be talking about a business. Function that in my opinion does get nearly enough attention. We look across industries of horizontal functions. Things like marketing come up or logistics. Come up customer service come up but what about sales and sales enablement in all honesty. There aren't that many companies chasing down this space. There's plenty of applications and sure there's a variety of startups but if we compare it to again marketing customer service of variety of other horizontal. There's just not as much action but the fact that matter is sales enablement exciting space. It's my perspective that a lot of revenue oriented applications. May i are gonna come after some of the efficiency or risk reduction applications in ai particularly for risk oriented bigger. Stodgy or enterprise firms but there sure is some upside to focusing on revenue when it comes to a adoption and companies who were succeeding in this space Showing that that's the case chorus dot. Ai is a company that has now raised over one hundred million dollars with record growth in twenty twenty and their ceo is jim benton. We speak with jim in this episode. Of the i in business podcast on what. The future of sales enablement looks like in other words. Would we have sales people on the phone or sending out emails or on linked in whatever the case may be how will they be. Ai augmented how will they be. Ai enabled and how will be able to consistently improve sales performance of sales people whether they're in business development or account managers or is it look like to consistently improve performance with artificial intelligence. It's very hard to get to one. Hundred million dollars raised unless you're showing real traction real results for your customers so i think. Jim's perspective is rather warranted on this matter because again there's not that many companies doing what they're doing at this time so i think there's a lot of insight and understanding what they're up you if you are interested in learning more. Ai applications in different horizontal verticals finding wear. Ai is finding its productive fit in the industry that be sure to check out. Emerged plus emerge pluses where you can access our full library. They i use cases as well as our library of a white papers. And our best practice guides for applying ai for our y for adoption for.

jim benton Ai jim Jim
"ai" Discussed on AI in Business

AI in Business

04:39 min | 3 months ago

"ai" Discussed on AI in Business

"To the a in business podcast. This is our third and last episode in a short three part series over the last three weeks on ai and the future of defense. We had steve blank on the program two weeks ago. Famed silicon valley innovator with Some rich military experience and a real mover and shaker in terms of changing defense culture mike brown the actual director of the dia iu proper and today we have the technical director f- artificial intelligence machine learning at the defense innovation unit. And that is none. Other than jared donman jared holds a phd in mechanical engineering and was a post doctoral fellow and computer science at stanford university before making his move to become technical director of ai. For the d. i you a little bit over a year ago. At the time of this recording and in this episode we focused less on the international dynamics of kind of building ai predominance between the west and china and we focus more on the range of use cases that are present when it comes to the broad topic of in this case homeland security and defense. Broadly as well. We chris and cross between the two jared opens. Our is a bit too. Just how many different kinds of ai. Projects the department of homeland security and the dod and up working on everything from mundane paper processing to healthcare related applications. And almost everything in between. There's so much to cover in the public sector that ends up under the dhs. And the dod. And jared gives us a nice lay of the land of those different kinds of applications. I think when people think defense they think ai for guiding missiles when it's really not like that at all there are smart phd's who leave excellent schools finest schools in the world And work in the public sector and don't end up working missiles for the bulk of their time. They work on a variety of other things that end up helping with homeland security. That might seem more mundane but also seem pretty interesting. There's some interesting niche. Use cases that jared covers that i think will be surprising for some folks who are thinking about ai indefens- or a in homeland security also talk a bit about jared's career path going from top university and into the public sector and what public sector folks might do to be able to recruit more smart folks like him. So if you're interested in ai in defense or if you're just interested in a wider array of ai use case understanding. I hope that this interview will be helpful for you so without further. Do this jared gunman. You're on the ai and business podcast. So jared.

jared donman jared steve blank ai mike brown dod dia stanford university department of homeland securit dhs china chris ai indefens west
"ai" Discussed on AI Today Podcast: Artificial Intelligence Insights, Experts, and Opinion

AI Today Podcast: Artificial Intelligence Insights, Experts, and Opinion

05:38 min | 11 months ago

"ai" Discussed on AI Today Podcast: Artificial Intelligence Insights, Experts, and Opinion

"Healthcare and for education in using more energy with the help of ai with the with the shoveling process and something that has been called the date wallet where where we can manage Getting enable citizens do manage their their data capital or something that is currently merging as as day to capital city. The three layers that we have come up with. And i think the the the key ovation is in the third layer that while we see a lot of countries that they they developed institutions and resources and In key areas where they want to focus on. This is something that came up. Maybe because of our Our structure of a lot of ideas coming from the bottom up that we need things that we can actually focus on and then showed results in for the everyday people that brings it closer to their to their lives and make it relatable. So maybe that's just a quick recap of the. I'm the strategy. Great yeah thank you. i know. It's a very comprehensive strategy. So it's not always easy to summarize just a few minutes that was great and then i know that follow up questions dig a little bit deeper into some of these including this one so one area of the strategy focuses on education competence development and societal preparedness. I know that there's also a goal in this strategy to create in a innovation centre as well. Can you share with us. Some more details around this picture so as i said this kind of competency development in social preparedness that it's a key factor that that the technology is there. We could gain a lot of value from this if a lot of people knew what this was. And we're not afraid of it. Did not have misconceptions are didn't have the first association to be the terminator seven big job to to transform a societies thinking from the judges of of what they haven't here to a realistic view of what is really happening although this is a very personal thing because an artificial we a lot of cases identify ourselves with our intelligence into..

ai
"ai" Discussed on AI Today Podcast: Artificial Intelligence Insights, Experts, and Opinion

AI Today Podcast: Artificial Intelligence Insights, Experts, and Opinion

03:26 min | 11 months ago

"ai" Discussed on AI Today Podcast: Artificial Intelligence Insights, Experts, and Opinion

"And we also wanted to to define some specific objectives that that's are going to be relevant for us. We had some high level objectives that we would like to achieve this fifteen percent of gdp growth for coming from a and also having one million people who are finding more value added jobs with the help of ai so not maybe not replacing drop. Maybe not new jobs. But having an ad on competence that they would be able to use a enabled tools are are can collaborate with robots in a way that they can be more focusing on the creative or the human league arts. This was a huge. Wouldn't say debate but it's a lot of ongoing discussion about how to how to do this. So the end result of of this. This kind of thinking was that we built a strategy in a three layer Format the first layer. The foundational layer were six pillars that we defined that may indicate from all the things that we learned from all other species and all of the things that how can we prepare the economy and society for this and the three retailers are what we call the value chain That how can we kick start the the data economy which was a huge work. How do we do the research and development. And how do we do the ai. Adoption or applications and this is a cycle that the more applications. We do the morning that we have more developments. We can have the more applications. We can have end and locally in the country and also in all of these stages integrating with available ecosystem in every part we defined new institutions new actions new resources that are allocated to different kind of purposes. The second sleep dealer is work. We called framing. Pillars are education end of this society the fourth pillar the infrastructural kind of investments and the regulatory unethical frameworks. Think these are kind of self explanatory. 'cause we have to invest in education either in a society as a whole either in changing the educational system. One of the big things that we were discussing is that. How can we manage the the education of the currently Working people since in ten years. Someone who already worked for ten years is gonna still be off time in. They're not even half time at their at their carrier. So it's not a level a question of changing in the universities or even at the at the elementary or high school levels that it's a.

ai
"ai" Discussed on AI Today Podcast: Artificial Intelligence Insights, Experts, and Opinion

AI Today Podcast: Artificial Intelligence Insights, Experts, and Opinion

04:15 min | 1 year ago

"ai" Discussed on AI Today Podcast: Artificial Intelligence Insights, Experts, and Opinion

"I need to to answer the question of how the data has been used. And for what purpose are they using the data, right? And I think that's where the question of wage. Ethical and responsible use of AI comes in. It doesn't matter what size organization you are. And also in what role you are. Right. So so only data scientist or did Engineers are not responsible to make sure that that it has been used properly. The machine learning models are being properly but it's 3 a.m. And every person has a responsibility to make sure that it is being used for its intended purposes. Yeah, that's super important. You know, I mean, I know that we had an entire conference focused around data for artificial intelligence because it's so important and foundational and you know, a lot of people are talking about data and how their data is being used and you know, what are data privacy issues and ethical issues and implications with all that so why is data privacy important and why is it something that should be considered through all stages of the data lifecycle and the Machine learning life cycle as well? Right, right. So so so with with the use of like, you know, as you know with AI and machine learning it has heavy Reliance off data rights. So so data is what powers all this machine learning models with the use of AI and ml right since we are supposed to more data sets right talk about social media, right? We're generating terabytes of data, right? So with the exposure to this much data and also accessibility to check data the question of data privacy and security has come to the Forefront right? It's not that it wasn't there earlier the specially how that data being used or who is using the data that thoughts Paramount now sites and and one of the things I think everybody needs to think about and coming back to that it it needs to be treated equally, right? So, yep. There needs to be an insecurity parallels, right? So it's always security by Design. So similarly data privacy or data security needs to be be be there by Design, right?.

AI scientist