Killer robots on the battlefield? — UN report sparks debate, Senate passes massive high tech competitiveness bill, and China unveils a GPT-3 rival

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Killer Robots Used in Libya? Not So Fast, Experts Say: A recent report from the UN Panel of Experts on Libya sparked concerns that a lethal autonomous weapons system (LAWS) — “killer robots,” colloquially — had been used in the field for the first time. The report, which detailed the fighting between the UN-recognized government and forces loyal to Khalifa Haftar, claimed that government forces had used a LAWS — namely the Turkish-made Kargu-2 drone — to track and engage Haftar-linked forces. The story was picked up by several major news outlets, with many framing it as a potential watershed in the history of autonomous weapons. But some experts — including Ulrike Franke, Jack McDonald and Michael Horowitz — pushed back, saying that the details in the report do little to distinguish the Kargu-2 system from loitering munitions, which have been in use for decades. As Zachary Kallenborn summarized in the Bulletin of the Atomic Scientists, the vague language of the UN report and the possibility that the Kargu-2’s manufacturer exaggerated its capabilities make it difficult to know how “autonomous” the lethal system was.
Chinese Researchers Announce the Largest “Large Language Model” Yet: A new natural language processing (NLP) model announced last week by the state-funded Beijing Academy of Artificial Intelligence (BAAI) is the largest ever trained. Wu Dao 2.0 has 1.75 trillion parameters — dwarfing GPT-3’s 175 billion parameters and even the 1.6 trillion parameters of Google’s Switch Transformer — and while the relationship between parameters and sophistication is not one-to-one, it is generally a good indicator of a model’s power. In addition to its high parameter count, Wu Dao 2.0 does more than just NLP — it is a multimodal system trained on 4.9 TB of text and images, meaning it can perform image recognition and generation tasks in addition to the text processing and generation tasks of traditional NLP. While BAAI has yet to publish a paper elaborating on the performance of Wu Dao 2.0, a handful of released results showed impressive performance: The model achieved state-of-the-art results on nine common benchmarks, surpassing previous juggernauts such as OpenAI’s GPT-3 and CLIP and Microsoft’s Turing-NLG.
Crypto Comes For Storage: A new cryptocurrency is driving up the price of computer storage, potentially making data storage for AI and machine learning more expensive. Cryptocurrency mining — the process of creating new coins or doing the computational work needed to maintain the network — has traditionally rewarded users who commit their computing power in what’s known as the “Proof of Work” (PoW) model. The new cryptocurrency, “Chia,” uses a “Proof of Space and Time” (PoST) model, rewarding users who store cryptographic numbers on their unused drive space. PoW cryptocurrency miners have been buying up high-end computer chips in droves, making the chips most essential for AI computing — namely GPUs, ASICs and FPGAs — more difficult to find. Chia’s PoST model, while more energy efficient, could do the same thing to storage drives. Although Chia only launched last month, there are already reports of hard drive shortages in Asia, and hard drive manufacturers have seen their stocks rise in anticipation of a rush on storage. With AI and machine learning relying on ever-larger datasets, a storage crunch could push training costs even higher.      
Government Updates

Senate Passes Massive China Competitiveness Bill: The Senate passed the U.S. Innovation and Competition Act by a vote of 68-32 on Tuesday, advancing a bill that, if signed into law, would pour more than $200 billion into U.S. R&D and high tech manufacturing. The bill has undergone a significant transformation since its introduction as the Endless Frontier Act in April. The original 160-page proposal grew, through a series of amendments, to more than 2,400 pages, and its price tag more than doubled. The bill now includes $52 billion in emergency appropriations to support semiconductor manufacturing and research incentives and includes a host of new provisions related to the U.S. research and development ecosystem. It directs $81 billion to the National Science Foundation, $29 billion of which is meant for a new directorate focused on emerging technology. Despite its bipartisan success in the Senate, observers expect the bill to face stiffer opposition in the House. The House Science and Foreign Affairs Committees continue to work on alternative proposals to the Senate-passed bill, with the House Science Committee reportedly preparing to consider its NSF for the Future Act next week. For more on this legislation and other Congressional news, see our latest legislative roundup.

Biden Budget Includes AI Funding: President Biden released his budget request late last month, a $6 trillion proposal with substantial funding for AI and emerging technology. While the request includes a significant increase in federal R&D funding — to $176.26 billion, a 9 percent increase compared to the FY2021 budget — many observers were surprised by the relatively small increases proposed for the DOD’s R&D budget. As Patrick Tucker noted in Defense One, the DOD’s $112 billion RDT&E budget request likely means an inflation-adjusted increase of only 0.6 percent. The department’s $874 million earmarked specifically for AI is also a far cry from the $8 billion recommended in the final report of the National Security Commission on Artificial Intelligence earlier this year. As Brandi Vincent noted in NextGov, the appendix published with the budget was relatively short on references to AI and other emerging technologies compared to previous budget requests. While that doesn’t mean AI and emerging technology research has been put on the back burner, as Bloomberg Government’s Chris Cornillie told Vincent, without more details, “it’s hard to know where we stand.”

White House Expands Blacklist of Chinese Companies: Last week, President Biden issued an executive order expanding restrictions on U.S. investment in companies with ties to the Chinese military, which President Trump originally imposed last November. The new order increases the number of targeted companies from 31 to 59 and revises the criteria for listed entities. Most notably, it aims to avoid litigation concerns by focusing on certain sectors of the Chinese economy — namely those that develop surveillance platforms used to “facilitate repression or serious human rights abuse” — rather than the DOD’s list of companies via NDAA Section 1237, under which President Trump’s November and January EO’s operated. Those EOs had faced hurdles in court, with several Chinese companies successfully challenging their inclusion in the list. Senior Biden administration officials reportedly said the changes in the new order were meant to ensure it had stronger legal footing. The new order will go into effect on August 2.

Pentagon Steers Toward Responsible AI Plan: Late last month, Deputy Secretary of Defense Kathleen Hicks issued a memo outlining the steps the Pentagon will take to implement “Responsible AI” (RAI) across the DOD. Last year, the Pentagon adopted five “DOD AI Ethical Principles,” (which we covered at the time). The new memo reaffirms those principles and lists key tenets the department will adhere to when implementing RAI, including ensuring trust between warfighters and AI systems and building an “RAI-ready” workforce. The memo names the Joint Artificial Intelligence Center as the coordinating body for RAI’s implementation and directs it to form and train an “RAI Working Council,” which will develop an RAI strategy and implementation plan, a talent management plan, and an AI acquisition plan over the coming months.

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