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) …
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.
The terms "artificial intelligence" and "machine learning" are often used interchangeably, but there's an important difference between the two. AI is an umbrella term for a range of techniques that allow computers to learn and act like humans. Put another way, AI is the computer being smart. Machine learning, however, accounts for how the computer becomes smart. But there's a reason the two are often conflated: The vast majority of AI today is based on machine learning.
June 21, 2021--Researchers in the Center for Research on Entertainment and Learning (CREL) at the University of California San Diego have developed a system to analyze and track eye movements to enhance teaching in tomorrow's virtual classrooms – and perhaps future virtual concert halls. UC San Diego music and computer science professor Shlomo Dubnov, an expert in computer music who directs the Qualcomm Institute-based CREL, began developing the new tool to deal with a downside of teaching music over Zoom during the COVID-19 pandemic. "In a music classroom, non-verbal communication such as facial affect and body gestures is critical to keep students on task, coordinate musical flow and communicate improvisational ideas," said Dubnov. "Unfortunately, this non-verbal aspect of teaching and learning is dramatically hampered in the virtual classroom where you don't inhabit the same physical space." To overcome the problem, Dubnov and Ph.D. student Ross Greer recently published a conference paper on a system that uses eye tracking and machine learning to allow an educator to make'eye contact' with individual students or performers in disparate locations – and lets each student know when he or she is the focus of the teacher's attention.