If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
For many gamers, E3 2021 hasn't churned out as many big announcements as expected. Many would say it was a disappointment so far, but Nintendo changed that with a bevy of game reveals and updates that was enough to satisfy fans. No, Nintendo didn't reveal an updated Switch as many predicted; instead, they surprised fans with the announcement of "Metroid Dread," a remake of its "Advance Wars" series, a new "WarioWare" entry and a Game & Watch for "The Legend of Zelda's" 35th anniversary. Oh, franchise supervisor Eiji Aonuma also showed off clips from a "Breath of the Wild" sequel. "Metroid Dread" was the biggest piece of news coming from the showcase -- While everyone was predicting a "Mario Kart 9" or "Pikmin 4," few were expecting a return of the the classic "Metroid" 2D gameplay.
While these technical skills are certainly important, we're also now looking more holistically at candidates to test their abilities to think critically and creatively as well as uncover new solutions. As we face new and unprecedented challenges in cyber protection, it's critical that cyber leaders hire team members who think outside-the-box, have intellectual curiosity, employ bold thinking, and are natural problem solvers. Protecting an organization against advanced cyber threats requires innovative thinking and techniques; people, process and technology capabilities are needed to properly defend ourselves against sophisticated attackers, such as nation states. Cyber threats will continue to evolve, as will the new techniques described above to enable cyber resiliency. Ariel Weintraub is currently the Head of Enterprise Cyber Security at MassMutual. Ariel first joined MassMutual in the fall of 2019 as the Head of Security Operations & Engineering, responsible for the Global Security Operations Center, Security Engineering, Security Intelligence, and Identity & Access Management. Prior to joining MassMutual, Ariel served as Senior Director of Data & Access Security within Cybersecurity Operations at TIAA where she led a three-year business transformation program to position IAM as a digital business enabler. Prior to TIAA, Ariel held the position of Global Head of Vulnerability Management at BNY Mellon and was part of the Threat & Vulnerability Management practice at PricewaterhouseCoopers (PwC).
Virtual Human Agents (VHA) can assist customers and staff in a multitude of ways. In retail and convenience stores, the AI-powered VHAs will help make shopping easier and more enjoyable on both sides of the counter. Imagine entering a convenience store. It's crowded, and the staff is overworked. You walk around the aisles for a while and finally find the things you need.
As massive amounts of data are stored every second, it allows for the opportunity to create meaningful and revolutionizing models. This data comes in several forms, including text, images and videos, all allowing for advanced models to be created using techniques such as Deep Learning. Further, using the extensive amount of data, applications using technologies such as computer vision are being used in products such as self-driving cars and facial recognition in phones. When creating a Deep Learning application, one of the first decisions to be made is where the model will be trained, either locally on a machine or through a third-party cloud provider. This is an important decision to be made as it could significantly impact the training time of a model.
In February 2021, Facebook launched a request for proposals (RFP) on sample-efficient sequential Bayesian decision-making. View RFP In a Q&A about the RFP, Core Data Science researchers said they are keen to learn more about all the great research that is going on in the area of Bayesian optimization. Eytan Bakshy and Max Balandat, members of the team behind the RFP, also spoke about sharing a number of really interesting real-world use cases that they hope can help inspire additional applied research and increase interest and research activity into sample-efficient sequential Bayesian decision-making. The team reviewed 89 high-quality proposals and are pleased to announce the two winning proposals below, as well as the 10 finalists. Thank you to everyone who took the time to submit a proposal, and congratulations to the winners.
As the health and safety of our candidates and our employees come first, we're excited to provide virtual experiences for interviews and new hire on-boarding. Dataminr puts real-time AI and public data to work for our clients, generating relevant and actionable alerts for global corporations, public sector agencies, newsrooms, and NGOs. Our real-time alerts enable tens of thousands of users at hundreds of public and private sector organizations to learn first of breaking events around the world, develop effective risk mitigation strategies, and respond with confidence as crises unfold. Dataminr is making its mark for growth and innovation, recently earning recognition on the Deloitte Technology Fast 500, Forbes AI 50 and Forbes Cloud 100 lists. We also earned accolades for'Most Innovative Use of AI' from the 2020 AI & Machine Learning Awards.
In his recent book The Myth of Artificial Intelligence: Why Computers Can't Think the Way We Do, AI researcher Erik J. Larson defends the claim that, as things stand today, there's no plausible approach in AI research that can lead to generalized, human-like intelligence. It's important to understand what the author is claiming- and what he's not claiming. He's not claiming that computers can never think like humans, as some philosophers of mind have claimed. Rather, his position is- if there's indeed a way to make computers think like humans, we haven't the foggiest what that is. Our current approaches- no matter how promising they might seem- are all dead ends. He contrasts this with the prevailing optimism about AI: the perception that current approaches are on the path to generalized intelligence, and the problems of this approach are, at least in theory, solvable. Thought this way, human-like computers seem just a matter of time. Larson, on the other hand, argues that even the fundamental theoretical principles of current AI approaches are non-starters. All of the current approaches in AI (or at least the most promising ones) are based on a certain model of thinking: inductive inference.
Responsible AI is a broad topic covering multiple dimensions of the socio-technical system called Artificial Intelligence. We refer to AI as a socio-technical system here as it captures the interaction between humans and how we interact with AI. In the first part of this series we looked at AI risks from five dimensions. In the second part of this series we look at the ten principles of Responsible AI for corporates. In this article we dive into AI Governance -- what do we really mean by governance?
On February 25, the Shanghai Government announced its Implementation Plan for Accelerating the Development of the New Energy Automobile Industry (2021-2025). It proposes that by 2025, smart cars with conditional self-driving functionalities shall enter large-scale production, significant progress will be made to set up a standard system for testing, demonstrating smart cars. City officials noted that so far, Shanghai has opened 560 kilometers of test roads. A total of 152 vehicles from 22 companies have been issued with road test and demonstration qualifications, which make Shanghai the first amongst other Chinese cities. We know you don't want to miss any news or research breakthroughs. Subscribe to our popular newsletter Synced Global AI Weekly to get weekly AI updates.
When viruses infect a cell, changes in the cell nucleus occur, and these can be observed through fluorescence microscopy. Using fluoresence images made in live cells, researchers at the University of Zurich have trained an artificial neural network to reliably recognize cells that are infected by adenoviruses or herpes viruses. The procedure also identifies severe acute infections at an early stage. In most cases, this does not lead to the production of new virus particles, as the viruses are suppressed by the immune system. However, adenoviruses and herpes viruses can cause persistent infections that the immune system is unable to keep completely in check and that produce viral particles for years. These same viruses can also cause sudden, violent infections where affected cells release large amounts of viruses, such that the infection spreads rapidly.