KataGo's world-class AI learned Go by playing millions of games against itself, but that still isn't enough experience to cover every possible scenario, allowing for vulnerabilities from unexpected behavior. In the world of deep-learning artificial intelligence (AI), the ancient board game Go looms large. Until 2016, the best human Go player could still defeat the strongest Go-playing AI. That changed with DeepMind's AlphaGo, which used deep-learning neural networks to teach itself the game at a level humans cannot match. More recently, KataGo has become popular as an open source Go-playing AI that can beat top-ranking human Go players.

Artificial intelligence (AI) continues to demonstrate its worth, innovating operations and optimizing workloads for organizations across all industries. As more industries look to harness the power of AI, we must be extra sensitive to the data we are using to train this technology. If we aren't, we risk backsliding against all the progress society has made in recent times in relation to intrinsic bias against Black, Indigenous, and People of Color (BIPOC). Businesses are using AI to venture into previously unexplored territory. Human-in-the-loop data training can take you a long way, but what about the cases in which we have no previous data?

Abstract: While automated driving technology has achieved a tremendous progress, the scalable and rigorous testing and verification of safe automated and autonomous driving vehicles remain challenging. This paper proposes a learning-based falsification framework for testing the implementation of an automated or self-driving function in simulation. We assume that the function specification is associated with a violation metric on possible scenarios. Prior knowledge is incorporated to limit the scenario parameter variance and in a model-based falsifier to guide and improve the learning process. For an exemplary adaptive cruise controller, the presented framework yields non-trivial falsifying scenarios with higher reward, compared to scenarios obtained by purely learning-based or purely model-based falsification approaches.

'All models are wrong, but some are useful' As this famous quote by George Box (known as the Box Theorem) shows, no model is ever going to be 100% accurate. If one is, run for the hills! Rather, models should be evaluated by their impact on the bottom line, or how useful they are to the business. In this blog post, we will explore a way in which models can be more useful, by embracing and leveraging uncertainty to maximize business results. Much of the time, business users want a single number to represent the'goodness' of a model, but machine learning models can tell us so much more than just a single number (like accuracy).

During the presidential elections in Russia last year, a candidate named "Alice" ran for president. She ran her campaign using a slogan like "the president who knows you best" and she did receive a couple thousand votes. To be correct, "Alice" was not a she, but an artificial intelligence (AI) system. Alice's campaign page is still up. "Alice" is not the only AI system to run for public office.

In this article, we consider the problem of choosing the optimal scenario of the impact between nodes based on of the introduced criteria for the optimality of the impact. Two criteria for the optimality of the impact, which are called the force of impact and the speed of implementation of the scenario, are considered. To obtain a unique solution of the problem, a multi-criterial assessment of the received scenarios using the Pareto principle was applied. Based on the criteria of a force of impact and the speed of implementation of the scenario, the choice of the optimal scenario of impact was justified. The results and advantages of the proposed approach in comparison with the Kosko model are presented.

Recently Anthony Evans, principal consultant with Computer Design & Integration, was recruited to come in halfway through what should have been a relatively straightforward project. The company wanted to deploy artificial intelligence at its customer service help desk to provide agents with a sort of "whisper agent" that would help the agents with questions about which they were unsure. Either the virtual agent would have the answer or it would escalate the question to a second tier of assistance. But something was off with the implementation pilot -- the whisper agent turned out to be only of marginal help to the desk agents. Eventually the team discovered where they went wrong, according to Evans.

Looking back at the history of artificial intelligence, it is plausible that we will witness similar setbacks in research interest or a temporary plateau in technological advancement, which might be caused by bottlenecks in computing power, quality of training data, or an inability to understand the outputs. In certain domains we might be able to develop systems to fully automate a cluster of tasks from end-to-end, like driving or financial modelling, but others might remain obscure for artificially intelligent systems.

When I started designing chatbots for BEEVA almost a year ago, I applied some of my UX knowledge and did some unsuccessful research looking for tools that could fit my needs. Actually, I was quite amazed that I couldn't find practical literature about the topic. There are tons of chatbots out there, but there's little about how companies really get hands on. I already shared some of my findings here, and here, with tools I found, general knowledge about designing chatbots and UX design applied on chatbots, but I think it would be great to make a deeper explanation about how I exactly face the situation on a regular basis. While many people immediately start thinking about how to manage the user flow, I separate my process into 4 different steps: the bot scope, the chatbot personality, a prioritized list of must-have features and the chatbot flow.

With advancements in AI reaching dizzying heights, the narrative around AI taking control away from humans is reaching fever pitch. What used to be thought of as uniquely human endeavors, such as writing poems, driving cars, composing music etc., are now being done by robots and in ways that are, in many instances, far superior to human performance. So as an intelligent species that has evolved over many thousands of years, how would humans, most likely, respond and adapt to this situation? Elon Musk has been vocal about the dangers of AI. He goes to the extent of saying that AI could be the likely cause of World War III, as countries ramp up their investments in AI.