Hackers have developed a'difficult to trace' new method to exploit AI tools inside workplace messaging app Slack -- tricking its chatbot into sending malware. The popular collaboration platform has gained prominence for facilitating quick communications between coworkers, with some linking it to a new age of'micro-cheating' and office affairs. The cybersecurity team within Slack's research program said Tuesday that they had patched the issue on the same day outside experts first reported the flaw to them. But the vulnerability, which lets hackers disguise malicious code inside uploaded documents and Google Drive files, highlights the growing risks posed by'artificial intelligence' that lacks the'street smarts' to deal with unscrupulous user requests. While the independent security researcher who first discovered the new flaw praised Slack for its diligent response, they went public with news of the AI's vulnerability'so that users could turn off the necessary settings to decrease their exposure.'

There has recently been widespread discussion of whether large language models might be sentient or conscious. Should we take this idea seriously? I will break down the strongest reasons for and against. Given mainstream assumptions in the science of consciousness, there are significant obstacles to consciousness in current models: for example, their lack of recurrent processing, a global workspace, and unified agency. At the same time, it is quite possible that these obstacles will be overcome in the next decade or so. I conclude that while it is somewhat unlikely that current large language models are conscious, we should take seriously the possibility that successors to large language models may be conscious in the not-too-distant future.

The New Brunswick Innovation Foundation (NBIF) is an independent funding corporation. In January, Jeff White, its CEO, Ginette Petitpas Taylor, Minister of Official Languages and Minister responsible for ACOA, and Jenica Atwin, Member of Parliament for Fredericton, officially launched the new "NBIF Artificial Intelligence (AI) Fund". The objective of the Fund is to bring together academic researchers and companies to collaborate. New Brunswick is a Canadian province of about 750,000 inhabitants, located south of Quebec, on the Atlantic coast. Artificial intelligence is, as in Canada (and all developed countries), considered a major strategic asset for economic growth, employment and innovation.

French start-up Iktos has announced a collaboration with Pfizer on the use of its artificial intelligence technology for drug design. This partnership comes in response to the considerable progress in the development of AI algorithms and computing power that has enabled the development of innovative approaches to small molecule drug design. Founded in 2016, Iktos develops generative AI technology in numerous collaborations with pharmaceutical and biotech companies. A fundamental aspect of the technology lies in the exploration of chemical space performed by generating compounds in silico under the constraints of the program's final objectives, rather than by screening compound libraries. As part of the collaboration, Pfizer has deployed Iktos' generative AI technology and is applying it to several small molecule research programs.

The Defense Advanced Research Projects Agency (DARPA) is setting its sights on developing an AI system with a detailed self-understanding of the time dimensions of its learned knowledge. DARPA's Time-Aware Machine Intelligence (TAMI) research program and incubator is looking to develop a new class of neural network architectures that incorporate an explicit time dimension as a fundamental building block for network knowledge representation," according to the TAMI program solicitation. The overall goal is to create an AI system that will be able to "think in and about time" when exercising its learned task knowledge in task performance. Current neural networks do not explicitly model the inherent time characteristics of their encoded knowledge. Consequently, state-of-the-art machine learning does not have the expressive capability to reason with encoded knowledge using time.

Impact Biomedical, a wholly-owned subsidiary of SGX-listed Singapore eDevelopment, has announced the initiation of Quantum, a research program designed as a solution to the'patent cliff', the impending pharmaceutical threat. A patent cliff looms when patents for blockbuster drugs expire without being replaced with new drugs, and pharmaceutical companies experience an abrupt decrease in revenue, reducing overall pharmaceutical innovation globally, including crucial research into new methods to prevent and treat illnesses. Impact, through their strategic partner Global Research and Discovery Group Sciences (GRDG), has created a solution called Quantum, a new frontier in pharmaceutical development. Quantum is a new class of medicinal chemistry that uses advanced methods to boost efficacy and persistence of natural compounds and existing drugs while maintaining the safety profile of the original molecules. Instead of modifying functional groups, as is typically done presently in drug discovery, this new technique alters the behavior of molecules at the sub-molecular level.

When the average person thinks about AI and robots what often comes to mind are post-apocalyptic visions of scary, super-intelligent machines taking over the world, or even the universe. The Terminator movie series is a good reflection of this fear of AI, with the core technology behind the intelligent machines powered by Skynet, referred to as an "artificial neural network-based conscious group mind and artificial general superintelligence system". However, the AI of today looks nothing like the worrisome science fiction representation. Rather, AI is performing many tedious and manual tasks and providing value from recognition and conversation systems to predictive analytics pattern matching and autonomous systems. In that context, the fact that governments and military organizations are investing heavily in AI shouldn't be as much concerning as it is intriguing. The ways that machine learning and AI are being implemented are both mundane from the perspective of enabling humans to do their existing tasks better, and very interesting seeing how machines are being made more intelligent to give humans better understanding and control of the environment around them.

Machine intelligence has been a military research goal for decades, but is it even worth it? Artificial intelligence research reaches toward long-held visions of human-machine symbiosis, and all the benefits this would have for military might. Even if scientists fall short of these lofty ambitions, or even if they prove impossible to fully achieve, aiming for them may move humanity further along the path of scientific progress -- but are small increments of progress worth billions of taxpayer dollars? Such ambitions for generic AI systems have fueled research programs across the defense landscape since the late 1960s. The Strategic Computing Program grew out of the context of the early 1980s --an optimism about the ability of computers to solve military problems coupled with the Reagan administration's Cold War push to bolster the United States through technology advancement and big defense budgets.

Motivated by recent interest in the status and consequences of competition between the U.S. and China in A.I. research, we analyze 60 years of abstract data scraped from Scopus to explore and quantify trends in publications on A.I. topics from institutions affiliated with each country. We find the total volume of publications produced in both countries grows with a remarkable regularity over tens of years. While China initially experienced faster growth in publication volume than the U.S., growth slowed in China when it reached parity with the U.S. and the growth rates of both countries are now similar. We also see both countries undergo a seismic shift in topic choice around 1990, and connect this to an explosion of interest in neural network methods. Finally, we see evidence that between 2000 and 2010, China's topic choice tended to lag that of the U.S. but that in recent decades the topic portfolios have come into closer alignment. Since then, a flurry of research has been conducted analyzing scientific publications in A.I. and machine learning in the U.S. and China, to probe the extent to which China has been able to follow through on this goal and overtake the U.S. as the "leader" in A.I. Some research, like Field Cady and Oren Etzioni's work with the Allen Institute for Artificial Intelligence, explores total publication volume partitioned by citation counts to compare both the quantity and quality of Chinese and American papers.[2] Using its own Scopus database, Elsevier has also done extensive bibliometric work, largely focused on defining the terms and topics of the artificial intelligence field.[3]

Chinese e-commerce giant Alibaba announced Wednesday it will invest more than $15 billion over the next three years into a global research and development program to increase collaboration and develop new technologies. That sum was slightly more than double the total amount Alibaba spent on R&D between 2014 and the fiscal year that ended March 31, 2017. The program is called the Academy for Discovery, Adventure, Momentum and Outlook -- DAMO Academy for short. As part of the program, Alibaba will set up seven research labs in Beijing, Hangzhou, San Mateo and Bellevue in the U.S., Moscow, Tel Aviv and Singapore, and recruit 100 researchers to staff them. Those labs will undertake projects in areas of data intelligence, Internet of Things, financial technologies, quantum computing and human-machine interaction, including machine learning and Natural Language Processing.