Even as Big Tech CEOs curry favor with President Trump, Silicon Valley employees are calling on their bosses to use their influence to help stop his immigration policies. The first Trump administration, and the tech industry that stood up to it, are both looking quainter by the day. Here's one example: In 2017, when President Trump issued a series of executive orders instituting a travel ban on foreigners from certain countries (predominantly Muslim-majority ones), people from across the United States vigorously protested the policy. They included some of tech's most elite: Google cofounder Sergey Brin, who joined a demonstration at the San Francisco airport; Amazon founder Jeff Bezos, who wrote a company-wide email outlining "legal options" that Amazon was considering to fight the ban; and Facebook founder Mark Zuckerberg, who took to Instagram to describe his own family's immigrant roots. On Saturday, hours after federal agents shot and killed ICU nurse Alex Pretti in the streets of Minneapolis, several prominent tech executives attended a private White House screening of, a documentary being released by (of course) Amazon MGM Studios. The timing was not lost on the group of Silicon Valley workers who recently launched ICEout.tech The letter, posted following Renee Nicole Good's killing earlier this month, has now been signed by more than 1,000 tech employees. Those workers, who come from across the spectrum of Big Tech companies and startups, are asking that executives use their clout to demand Immigration and Customs Enforcement agents leave American cities, that they cancel company contracts with the agency, and that they speak publicly about ICE's violent and deadly tactics. Worker-led demands like those were commonplace during Trump 1.0, when tech employees at the world's biggest companies often spoke out--internally and externally--about the cruelty of the US administration and the industry's role in facilitating or tempering its most craven policies. Meanwhile, the executives leading those companies have been busy kissing the ring-- over dinner at the White House or with outlandishly expensive documentaries nobody's watching--at every opportunity. Is the dam finally breaking? This week, Silicon Valley leaders including Anthropic heads Dario and Daniela Amodei, OpenAI CEO Sam Altman, and Apple CEO Tim Cook finally spoke out about ICE's outrageous overreach.

While many tech workers protested President Donald Trump's policies during his first term, Silicon Valley's rank and file has been quieter over the past year as their bosses genuflect to his administration. But that may be changing following the killings of Renee Good and Alex Pretti in Minneapolis. Last week, following the killing of Good, more than 200 Silicon Valley staffers published a letter urging tech leaders to use their platforms to call for ICE's removal from U.S. cities. As of Tuesday, following the killing of Pretti, the letter has more than 450 signatories, including employees from Google, Amazon and TikTok. The letter argues that tech leaders have a unique ability to influence Trump.

Embedding as a Service (EaaS) has become a widely adopted solution, which offers feature extraction capabilities for addressing various downstream tasks in Natural Language Processing (NLP). Prior studies have shown that EaaS can be prone to model extraction attacks; nevertheless, this concern could be mitigated by adding backdoor watermarks to the text embeddings and subsequently verifying the attack models post-publication. Through the analysis of the recent watermarking strategy for EaaS, EmbMarker, we design a novel CSE (Clustering, Selection, Elimination) attack that removes the backdoor watermark while maintaining the high utility of embeddings, indicating that the previous watermarking approach can be breached. In response to this new threat, we propose a new protocol to make the removal of watermarks more challenging by incorporating multiple possible watermark directions. Our defense approach, WARDEN, notably increases the stealthiness of watermarks and has been empirically shown to be effective against CSE attack.

TinyML is about to get really big, or at least that's what a startup thinks, as we explain on this week's podcast. Useful Sensors is the company that's making inexpensive, low-powered edge sensors in a way that protects privacy. We discuss why we agree with that approach. Next up are our thoughts on why 5G really hasn't taken the IoT market by storm yet. You'll want to hear our reasons for this because there are several.

Is it $61 billion and 38.4% CAGR by 2028 or $43 billion and 37.4% CAGR by 2027? Depends on which report outlining the growth of edge computing you choose to go by, but in the end it's not that different. What matters is that edge computing is booming. There is growing interest by vendors, and ample coverage, for good reason. Although the definition of what constitutes edge computing is a bit fuzzy, the idea is simple.

Artificial intelligence has become an integral part of the day-to-day operations across most industries. And in great part, AI can be credited with condensing vast amounts of data into something more usable. But as companies come under greater public scrutiny for how algorithms are influencing corporate behavior, the question of how to ethically apply artificial intelligence is top of mind for commercial insurance leaders. Ethical use of technology is "not a problem that's exclusive to AI," Anthony Habayeb, founding CEO of Monitaur, an AI governance company, said. "Corporations have their corporate governance and need to have their opinion of what sort of ethics and practices they bring into the market as a company. And those principles should be implemented in their AI, software and practices overall," he explained.

Pre-trained language models derive substantial linguistic and factual knowledge from the massive corpora on which they are trained, and prompt engineering seeks to align these models to specific tasks. Unfortunately, existing prompt engineering methods require significant amounts of labeled data, access to model parameters, or both. We introduce a new method for selecting prompt templates without labeled examples and without direct access to the model. Specifically, over a set of candidate templates, we choose the template that maximizes the mutual information between the input and the corresponding model output. Figure 1: Performance of template selected by our maximum Across 8 datasets representing 7 distinct NLP mutual information method (MI) compared to tasks, we show that when a template has high the the worst, mean, median, and best prompt on GPT-3 mutual information, it also has high accuracy Davinci (175B). Our method performs at almost oracle on the task. On the largest model, selecting levels, without labels or access to model weights.

For all the promise and peril of artificial intelligence, there's one big obstacle to its seemingly relentless march: The algorithms for running AI applications have been so big and complex that they've required processing on powerful machines in the cloud and data centers, making a wide swath of applications less useful on smartphones and other "edge" devices. Now, that concern is quickly melting away, thanks to a series of breakthroughs in recent months in software, hardware and energy technologies that are rapidly coming to market. That's likely to drive AI-driven products and services even further away from a dependence on powerful cloud-computing services and enable them to move into every part of our lives -- even inside our bodies. In turn, that could finally usher in what the consulting firm Deloitte late last year called "pervasive intelligence," shaking up industries in coming years as AI services become ubiquitous. By 2022, 80% of smartphones shipped will have AI capabilities on the device itself, up from 10% in 2017, according to market researcher Gartner Inc.

This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. Deep learning models owe their initial success to large servers with large amounts of memory and clusters of GPUs. The promises of deep learning gave rise to an entire industry of cloud computing services for deep neural networks. Consequently, very large neural networks running on virtually unlimited cloud resources became very popular, especially among wealthy tech companies that can foot the bill. But at the same time, recent years have also seen a reverse trend, a concerted effort to create machine learning models for edge devices.

This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. Deep learning models owe their initial success to large servers with large amounts of memory and clusters of GPUs. The promises of deep learning gave rise to an entire industry of cloud computing services for deep neural networks. Consequently, very large neural networks running on virtually unlimited cloud resources became very popular, especially among wealthy tech companies that can foot the bill. But at the same time, recent years have also seen a reverse trend, a concerted effort to create machine learning models for edge devices.