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AI is real now: A conversation with Sophie Vandebroek
More times than almost any other field of innovation, artificial intelligence has weathered recurring cycles of overinflated hope, followed by disappointment, pessimism, and funding cutbacks.
But Sophie Vandebroek, IBM’s vice president of emerging technology partnerships, thinks the AI winters are truly a thing of the past, thanks to the huge amounts of computing power and data now available to train neural networks.
And she describes the mission of the new MIT-IBM Watson AI Lab, a $240 million, 10-year collaboration between IBM researchers and MIT faculty and students to focus on the core advances that will make AI more useful and reliable across industries from healthcare to finance to security.
Darktrace has built innovative machine learning technology can spot unusual activity using an approach modeled on the human immune system.
In the second half of the show, Darktrace CEO Nicole Eagan explains how Darktrace’s technology works and why companies need to bring new defenses to today’s cyber arms race.
It’s the locus of more than 50 new projects involving IBM researchers and MIT faculty, all aimed at advancing the fundamental technologies behind artificial intelligence.
Elizabeth: Sophie’s currently IBM’s vice president of emerging technology partnerships, and she’s known in the computing industry for her distinguished history pushing innovation forward—not only at IBM but at Xerox, where she spent over a decade as the chief technology officer at Xerox.
Today there are 10 billion transistors on a centimeter-square chip that IBM develops today, and that compute power has resulted in the mobile devices we have in our pockets, the latest high performance computer, the Summit, you know, that IBM computer that Oak Ridge National Lab purchased recently.
And so the data on the web together with the structured digital data that many enterprises have today, many enterprises have started to digitize all their work processes together with all the data comes from sensors with the Internet of Things and sensors and manufacturing and cameras, ubiquitous cameras, etc.
And a huge amount of progress has been made in the neural networks over the last five years, since, for the first time in 2012, it was a deep learning neural network which was running on a graphical processing unit, a GPU, that for the first time won a competition for image recognition.
And in fact I would say artificial intelligence itself is now at the beginning of an exponential curve, that we are creating exponentially fast new insights that individuals, no matter what industry you’re in, can use to make fast, real-time decisions.
For example, virtual agents which fit in this category of narrow AI which we just passed that phase, we’re in the phase of broad AI today and we can talk about that before we will get to general artificial intelligence.
In fact some of these virtual agents today will very quickly assess whether you’re an extrovert or introvert and adjust their language according to your style.
for example if you drive your car through a tollbooth today it’s totally automatic license plate recognition and reading of the license plates such that within the backend processes, you actually get charged for driving through the toll booths.
It’s also starting to become top of mind of boards and directors of companies, to make sure that these risks related to deploying and embracing artificial intelligence as part of the organization are addressed.
Number one is making sure that the algorithms, AI algorithms are fair—that the outcome of the AI algorithm, as AI assists humans in making decisions, that the decisions are fair and ethical and not biased.
So we just launched, open source, anybody can help us improve it, so IBM research open-sourced the AI Fairness 360 Toolkit, where you can pull in your algorithm and then it’s checked for all kind of biases.
Some of the reasons for the bias is that the data set with which the argument is trained, and especially in enterprises—enterprises don’t have huge volumes of data like in the consumer world, right, where there could be huge amount of cat pictures to train an image algorithm to recognize a cat.
The enterprises where a company, let’s say a hospital or a school or an enterprise, has a limited amount of data to train the algorithms so the data might not have sufficient amount of diversity and inclusion within the dataset, so that in fact the algorithms become biased.
And so if your source for software developers leveraging an AI algorithm that might be trained on your data, the algorithm will learn that most of the software developers are male, because that’s what you hired in the past.
Maybe four weeks if I had if I include the lunch Anantha and I had before our senior vice president talked to the president of MIT on a Monday morning and three weeks later on a Friday, the contract was signed.
And so it is indeed, the vision was to create this joint lab of about 100 researchers, and the researchers included IBM researchers, MIT professors, and students, and we celebrated the first anniversary in September.
Or learning from small data, different methodologies to learn from small data, like hospitals have a small set of patients but a small set of data.
And now that we celebrated our first anniversary, we have just agreed between MIT and IBM that we will open our doors for other large enterprises that are truly interested to be at the cutting edge of research in artificial intelligence to join our lab.
And I guess the question I’d have is and how do we think about blockchain as being more than a curiosity and actually something that’s trustworthy and stable and can kind of enhance the business context in which it’s used?
When at IBM when we talk about blockchain, in fact blockchain, a lot of research was happening in blockchain for several years in the research labs, and IBM created a business now about two and a half years ago, a blockchain business unit.
The next one is having blockchain, this underlying platform, being used in value chains to track valuable goods or valuable digital goods as they go from where they originate to where they are being used.
But what enterprises are interested in is to be able to create trusted transactions among partners that inherently that might not know each other like small businesses or larger businesses or distributors or farmers.
So if you think about the human body’s immune system, it has an innate sense of self that allows it to know what’s not self and have a very precise and rapid response.
Elizabeth: And how do you see in general, more generally, cyber attacks changing these days, be they coming out of nation states or out of individual bad guys, cyber criminals?
He said “Just think, there’s a team somewhere else in the world and that team’s full time job is thinking about how to either steal your intellectual property or somehow get information from you.” And that’s really what companies are up against, and the reason for that is the kind of cyber arms race where we’re used to governments fighting against governments—while that’s still taking place, we now have this whole new dimension where nation states are actually possibly attacking the companies.
In fact in many cases there’s just new strains of attacks where a single line of code is changed, and now what’s called the signatures no longer match.
So in our case we are using of numerous types of unsupervised, supervised, and deep learning to be able to not only find the attacks but have the artificial intelligence know how to investigate the attack.
But the other thing we did find, from a practical perspective, is that it does take time for people in the security organization—maybe this is the first time they’re even working with artificial intelligence and being augmented—it takes some time for them to actually build that trust.
And once the humans start seeing, wow it’s making the right recommendation every time, they build a trust and they put it into what we call active mode.
So I think having done this now over the past five years across nearly 2,500 companies, we’ve gotten really good at understanding what it takes to build that trust relationship but also our algorithms have gotten really strong and really smart at responding to these attacks in real time.
Nicole: You’re absolutely right, although it’s kind of early days and we’ve only seen indications that it can go in that direction, and we’ve seen things like behavioral attacks where the AI might learn, actually, your style and mode of communication that you use let’s say an email.
And I wonder if beyond sort of cybersecurity, if you’ve thought about looking at normal activity to help with other kinds of things, like say regulatory compliance or risk management, things like that.
So each one of them who uses Darktrace for security today actually has embedded artificial intelligence that’s learning the sense of self and is continuously learning and updating.
So absolutely, I think although today we kind of are only unlocking the power of that dataset and our AI models for cyber security, we could make a decision in the future to help customers use other keys to unlock it to deliver additional value.
With artificial intelligence (AI) recommendations driving 75% of Netflix selections and 35% of Amazon purchases, businesses must start looking to market to their newest customer – AI.
Consumers will welcome the opportunity to leverage AI to interact with brands to purchase, for example, “one pack of plain white paper towels from the vendor offering the best deal.” My favorite example is “get me some new blue pens,” where the AI uses my personal order history and reviews to order my pens instead of picking the first set of blue pens from a list.
That said, there is a type of shopping we’ll classify as “background shopping,” where customers consider the items routine purchases or even subscription-worthy and can be done on a repeatable basis – think toilet paper, domestic shopping, house cleaning items, etc.
Companies have a delicate balance and choice going forward. Companies that want to avoid falling into the background shopping category will have to make an even greater effort to determine what makes certain consumers think about shopping for a product as a meaningful experience rather than just a chore or menial task.
Organizations must take a strong look at what can set them apart from competitors, whether it’s eco-friendly processes to resonate with green shoppers, unscented formulas for sensitive skins, or industrial-sized packs of goods for a big family.
This is the next generation of brick-and-mortar trade promotion management: while manufacturers used to pay retailers for the best spots on the shelves, we’re now looking at a digital version to drive product placement within the AI platform.
IBM's Watson Anywhere lets customers run AI on any cloud they want, and it's a sign that IBM is pulling back from plugging its own cloud
Instead of losing its breath trying to keep up with the top three leaders in the cloud race, IBM is now taking a more flexible approach, analysts say.
On Feb. 12, IBM announced Watson Anywhere, which allows people to use the company's Watson artificial intelligence on any cloud they want, whether it's a public cloud, a private cloud, or hybrid cloud -- a combination of cloud and data centers.
Watson Anywhere is optimized for IBM's cloud, but the fact that it can also run on any other cloud is a sign that IBM is looking to capitalize on important market trends, such as customers who want to use multiple clouds, says Sid Nag, research director at Gartner.
'We're doing one better where we're going to take our technologies and overlay that not just on IBM cloud, but also the other cloud providers like Amazon, Google, Azure and others.
Since analysts say it's unlikely that IBM's cloud will reach the scale of Amazon Web Services, Microsoft or Google anytime soon, they see it as an effective strategy that shows IBM is responding to what customers need.
'I think making it available in whatever platform the end user wants to use it on is a very good strategy...You want your core software products used in as many places as possible.'
Dave Bartoletti, vice president and principal analyst at Forrester, says Watson Anywhere is somewhat similar to Google's approach of making its AI services, like TensorFlow, available to run anywhere.
'The IBM public cloud has never reached the scale of AWS or Azure, so IBM can't afford to limit the potential of Watson to its own cloud,' Bartoletti told Business Insider.
'IBM's betting that Watson can compete with native public cloud AI services well enough to generate revenue, and that it doesn't make sense anymore to tie Watson to IBM public cloud.'
On the downside, Nag questions whether customers will choose to use Watson, instead of artificial intelligence services that are already provided by the cloud they're using, such as Amazon Rekognition.
'IBM's Watson functionalities works on multiple clouds, so that's definitely an advantage, but it's going to be a decision making process on behalf of the buyer,' Nag said.
Right now, many companies still have to keep some workloads in their in-house data centers due to regulations, and the only top 3 cloud provider that has a generally available hybrid cloud service is Microsoft.
In the near term, analysts sees IBM focusing on its software and consulting services that help customers manage different clouds, rather than pushing its own cloud forward.
'IBM is saying, 'We're going to meet the customer where they are, give them choices and gain more revenue to the service rather than build a public cloud,'' Nag said.
IBM Watson Health Teams Up with Hospitals for AI, EHR Research
February 20, 2019 -IBM Watson Health has announced a ten-year, $50 million investment in artificial intelligence (AI) research partnerships with Brigham and Women’s Hospital (BWH) and Vanderbilt University Medical Center (VUMC).
“Building on the MIT-IBM Watson Lab announced last year, this collaboration will include contributions from IBM Watson Health's longstanding commitment to scientific research and our belief that working together with the world's leading institutions is the fastest path to develop, advance, and understand practical solutions that solve some of the world's biggest health challenges,”
“By putting the full force of our clinical and research team together with two of the world's leading academic medical centers, we will dramatically accelerate the development of real-world AI solutions that improve workflow efficiencies and outcomes.”
“Through AI, we have an opportunity to do better, and our hope is to find new ways through science and partnerships with industry leaders like Watson Health to unlock the full potential of AI to improve the utility of the EHR and claims data to address major public health issues like patient safety.”
The academic medical center has engaged in numerous partnership and research projects aimed at a variety of challenging issues, including streamlining workflows, leveraging genomic data to personalize care, and reducing care disparities.
- On 21. oktober 2021
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