Organizations of all sizes have accelerated the rate at which they employ AI models to advance digital business transformation initiatives. But in the absence of any clear-cut regulations, many of these organizations don't know with any certainty whether those AI models will one day run afoul of new AI regulations. Ted Kwartler, vice president of Trusted AI at DataRobot, talked with VentureBeat about why it's critical for AI models to make predictions "humbly" to make sure they don't drift or, one day, potentially run afoul of government regulations. This interview has been edited for brevity and clarity. VentureBeat: Why do we need AI to be humble?

Most medical articles have methods & results sections and matches in those sections are more important. I had little to no expectations entering this competition, so I wouldn't say I was surprised by anything. It was great to see so many smart and capable people all working together to try to help in whatever way they could. All of the work is driven by the Kaggle platform. The list of notebooks cover all the submissions for Round 1 and Round 2 of the CORD-19 challenge. All of the notebooks are in Python.

That's how Bethesda Game Studios Executive Producer Todd Howard described the upcoming game "Starfield" in an exclusive video interview with The Washington Post. "Starfield," in development since 2017, represents the first new IP in 25 years for Bethesda, the video game studio behind the critically acclaimed action role-playing series "The Elder Scrolls" and "Fallout." The new space-based role-playing game will be released Nov. 11 of 2022 for PC and Xbox Series X and S. Because of major technological leaps since "Skyrim," the last Elder Scrolls installment from 2011, Howard said "Starfield" will be much more robust than that title, which won numerous game of the year awards. The first video footage of "Starfield," which kicked off the Microsoft/Bethesda joint presentation during the Electronic Entertainment Expo (E3) Sunday, shows a fraction of the breadth of this brand new universe. How big could it get?

We recently chatted with Susanna Dillenbeck, Commercial Partnerships Manager at Furhat Robotics, to learn more about the impact Conversational AI and Social Robots are having on our lives, and their potential for the future. Furhat Robotics is a Stockholm-based startup building the world's most advanced social robotics and conversational AI platform. Hi Susanna, thank you so much for joining us for the RE•WORK Woman in AI Podcast today. I wanted just to firstly ask if you could tell us a bit about the company that you work for, which is Furhat Robotics. It was founded in 2014 I believe. Yes, exactly, so we are a social robotics startup from Sweden originating from the Royal Institute of Technology here in Stockholm, and the company was actually never meant to be a company. So we are the result of the research of our four founders and our current CEO, this was also his PhD project, and it just turned out that the research that we made within social robotics was quite groundbreaking and people from all over the world started reaching out to the group and saying, OK, where can we buy your robot or your prototype? And then the company was born as a demand out of that.

David Eagleman, 50, is an American neuroscientist, bestselling author and presenter of the BBC series The Brain, as well as co-founder and chief executive officer of Neosensory, which develops devices for sensory substitution. His area of speciality is brain plasticity, and that is the subject of his new book, Livewired, which examines how experience refashions the brain, and shows that it is a much more adaptable organ than previously thought. For the past half-century or more the brain has been spoken of in terms of a computer. What are the biggest flaws with that particular model? But in fact, what we're looking at is three pounds of material in our skulls that is essentially a very alien kind of material to us.

I am Mrignayni, a content strategist and copywriter for B2B tech companies of the world. Earlier, widespread automation meant the introduction of service sector jobs or big stupid machines doing repetitive work in factories. That's not the case a couple of decades down the line, is it? Machines are now capable of breaking down complex jobs into smaller, predictable ones that need very little specialization. What is the future of automation?

Convolutional Neural Networks (CNN) have shown impressive performance in computer vision, natural language processing, and many other applications, but they exhibit high computations and substantial memory requirements. To address these limitations, especially in resource-constrained devices, the use of cloud computing for CNNs is becoming more popular. This comes with privacy and latency concerns that have motivated the designers to develop embedded hardware accelerators for CNNs. However, designing a specialized accelerator increases the time-to-market and cost of production. Therefore, to reduce the time-to-market and access to state-of-the-art techniques, CNN hardware mapping and deployment on embedded accelerators are often outsourced to untrusted third parties, which is going to be more prevalent in futuristic artificial intelligence of things (AIoT) systems. These AIoT systems anticipate horizontal collaboration among different resource-constrained AIoT node devices, where CNN layers are partitioned and these devices collaboratively compute complex CNN tasks Therefore, there is a dire need to explore this attack surface for designing secure embedded hardware accelerators for CNNs. Towards this goal, in this paper, we exploited this attack surface to propose an HT-based attack called FeSHI. This attack exploits the statistical distribution i.e., Gaussian distribution, of the layer-by-layer feature maps of the CNN to design two triggers for stealthy HT with a very low probability of triggering. To illustrate the effectiveness of the proposed attack, we deployed the LeNet and LeNet-3D on PYNQ to classify the MNIST and CIFAR-10 datasets, respectively, and tested FeSHI. The experimental results show that FeSHI utilizes up to 2% extra LUTs, and the overall resource overhead is less than 1% compared to the original designs

Can we expect complete automation of medical processes in the near future, given the issues that AI systems face even in the most advanced areas of healthcare? I have touched on some of the aspects in my previous article, so now let's talk about more challenges facing the development and implementation of AI in the healthcare industry. The development of intelligent technologies is directly related to the method of creating AI -- or, more precisely, to the peculiarities of its training and processing the received data. The most typical tools, in this case, are neural networks and machine learning algorithms loaded with data from clinical databases and supported with the information about types of diagnostics and care provided. This mandates a good level of interaction between AI and actual databases and simultaneously triggers questions about the amount, versatility and representativeness of the cases included.

On March 18, 2018, at around 10 p.m., Elaine Herzberg was wheeling her bicycle across a street in Tempe, Arizona, when she was struck and killed by a self-driving car. Although there was a human operator behind the wheel, an autonomous system--artificial intelligence--was in full control. This incident, like others involving interactions between people and AI technologies, raises a host of ethical and proto-legal questions. What moral obligations did the system's programmers have to prevent their creation from taking a human life? And who was responsible for Herzberg's death? "Artificial intelligence" refers to systems that can be designed to take cues from their environment and, based on those inputs, proceed to solve problems, assess risks, make predictions, and take actions. In the era predating powerful computers and big data, such systems were programmed by humans and followed rules of human invention, but advances in technology have led to the development of new approaches.

Earlier, widespread automation meant the introduction of service sector jobs or big stupid machines doing repetitive work in factories. That's not the case a couple of decades down the line, is it? Machines are now capable of breaking down complex jobs into smaller, predictable ones that need very little specialization. We are on the verge of being outcompeted! What is the future of automation?