We analyze the sample complexity of full-batch Gradient Descent (GD) in the setup of non-smooth Stochastic Convex Optimization. We show that the generalization error of GD, with common choice of hyper-parameters, can be $\tilde \Theta(d/m+1/\sqrt{m})$, where d is the dimension and m is the sample size. This matches the sample complexity of \emph{worst-case} empirical risk minimizers. That means that, in contrast with other algorithms, GD has no advantage over naive ERMs. Our bound follows from a new generalization bound that depends on both the dimension as well as the learning rate and number of iterations. Our bound also shows that, for general hyper-parameters, when the dimension is strictly larger than number of samples, $T=\Omega(1/\epsilon^4)$ iterations are necessary to avoid overfitting. This resolves an open problem by Schlisserman et al.23 and Amir er Al.21, and improves over previous lower bounds that demonstrated that the sample size must be at least square root of the dimension.

We analyze the sample complexity of full-batch Gradient Descent (GD) in the setup of non-smooth Stochastic Convex Optimization. We show that the generalization error of GD, with common choice of hyper-parameters, can be \tilde \Theta(d/m 1/\sqrt{m}), where d is the dimension and m is the sample size. This matches the sample complexity of \emph{worst-case} empirical risk minimizers. That means that, in contrast with other algorithms, GD has no advantage over naive ERMs. Our bound follows from a new generalization bound that depends on both the dimension as well as the learning rate and number of iterations.

Consider a setting where a pre-trained agent is operating in an environment and a human operator can decide to temporarily terminate its operation and take-over for some duration of time. These kind of scenarios are common in human-machine interactions, for example in autonomous driving, factory automation and healthcare. In these settings, we typically observe a trade-off between two extreme cases -- if no take-overs are allowed, then the agent might employ a sub-optimal, possibly dangerous policy. Alternatively, if there are too many take-overs, then the human has no confidence in the agent, greatly limiting its usefulness. In this paper, we formalize this setup and propose an explainability scheme to help optimize the number of human interventions.

Temporal Graph Neural Networks, a new and trending area of machine learning, suffers from a lack of formal analysis. In this paper, information theory is used as the primary tool to provide a framework for the analysis of temporal GNNs. For this reason, the concept of information bottleneck is used and adjusted to be suitable for a temporal analysis of such networks. To this end, a new definition for Mutual Information Rate is provided, and the potential use of this new metric in the analysis of temporal GNNs is studied.

With Artificial Intelligence on the rise, human interaction with autonomous agents becomes more frequent. Effective human-agent collaboration requires that the human understands the agent's behavior, as failing to do so may lead to reduced productiveness, misuse, frustration and even danger. Agent strategy summarization methods are used to describe the strategy of an agent to its destined user through demonstration. The summary's purpose is to maximize the user's understanding of the agent's aptitude by showcasing its behaviour in a set of world states, chosen by some importance criteria. While shown to be useful, we show that these methods are limited in supporting the task of comparing agent behavior, as they independently generate a summary for each agent. In this paper, we propose a novel method for generating contrastive summaries that highlight the differences between agent's policies by identifying and ranking states in which the agents disagree on the best course of action. We conduct a user study in which participants face an agent selection task. Our results show that the novel disagreement-based summaries lead to improved user performance compared to summaries generated using HIGHLIGHTS, a previous strategy summarization algorithm.

Healthcare systems have adopted artificial intelligence in fits and starts. For years, emergency rooms have haltingly tested AI systems that collect information on patients' symptoms and medical histories, weigh it against data about similar cases, and make recommendations about who should be rushed in for treatment first. Doctors see the potential, but are wary of algorithms that don't have years of medical training. But the risk of Covid-19 transmission in ERs, along with shortages of staff and resources, have left some hospitals with no choice. The pandemic has dramatically accelerated the use of AI triage.

This is the code for my article "Coloring B&W portraits with neural networks" Earlier this year, Amir Avni used neural networks to troll the subreddit /r/Colorization - a community where people colorize historical black and white images manually using Photoshop. They were astonished with Amir's deep learning bot - what could take up to a month of manual labour could now be done in just a few seconds. I was fascinated by Amir's neural network, so I reproduced it and documented the process. Read the article to understand the context of the code. If you are new to FloydHub, do their 2-min installation, check my 5-min video tutorial or my step-to-step guide - it's the best (and easiest) way to train deep learning models on cloud GPUs.

This week at the Intel Shift Conference in New York, I had the opportunity to listen to my colleague Amir Khosrowshahi, CTO of the Intel AI Products group, speak to a gathering of business executives about the transformative impacts of AI. Amir explained how artificial intelligence (AI) can change what organizations do and how they do it, creating new business opportunities. Every company is in some phase of their AI adoption course: evaluating and understanding the opportunities, testing AI use cases and its outcome on their business, or fully integrating AI systems that are increasingly driving business metrics. AI concepts have been around for more than 60 years, but we now have the technology to make AI a reality. AI is predicated on the simple idea that with the right training a computer can simulate human decision making.

Our challenge to bot builders; design, develop, and market the best bots you can that utilise the Hu:toma API. There will be hurdles to jump along the way, which is why we've scouted the bot community and brought together some of the smartest, most experienced minds to coach & provide feedback to our teams. Over a period of 16 weeks, that's right someone will be getting a rather large christmas present, competition entrants will have six categories of bots to compete in building. Category deadlines are spaced every two weeks, with the six category winners and six wildcards making it to the final round. These categories are closely aligned with Amir Shevat's book "Designing Bots".

Identifying what your customers are looking for is an essential part of any business that wants to succeed. If you're not selling something that people want, you're not going to sell much. But identifying those factors has long been difficult and painstaking, and taken many hours of manpower that was necessary elsewhere. With the invention of chatbots, finding out what your customers are looking for is easier than ever. Chatbots can identify and analyse never-before-seen data on a scale that is truly helpful.