Dear friends,
I’m grateful to the AI community for the friendships it has brought me and the benefits it has brought to billions of people. But members of the AI community don’t always honor one another. In the spirit of Thanksgiving, which we in the U.S. celebrate this week, I’d like to talk about how we can treat each other with greater civility.
To be clear, the AI world has problems. I don’t want anyone to shy away from addressing them. When you come across a pressing issue, here are suggestions that might encourage productive conversation:
As we wrestle with important issues around values, ethics, diversity, and responsibility, let’s keep our arguments civil and support discussions that focus on solving problems rather than public shaming. In addition to being civil yourself, I ask you also to encourage others to be civil, and think twice before repeating or amplifying messages that aren’t. The AI community faces difficult challenges, and working together will make us more effective in wrestling with them.
Happy Thanksgiving and keep learning! Andrew
NewsWhen Officials Share Personal DataThe government of South Korea is supplying personal data to developers of face recognition algorithms. What’s new: The South Korean Ministry of Justice has given the data profiles of more than 170 million international and domestic air travelers to unspecified tech companies, the news service Hankyoreh reported. The distribution of personal data without consent may violate the country’s privacy laws. How it works: The government collects data on travelers at Incheon International Airport, the country’s largest airport. It gives facial portraits along with the subjects’ nationality, gender, and age to contractors building a system that would screen people passing through Incheon’s customs and immigration facility. The project began in 2019 and is scheduled for completion in 2022.
Why it matters: Face recognition is an attractive tool for making travel safer and more efficient. But data is prone to leaking, and face recognition infrastructure can be pressed into service for other, more corruptible purposes. In the South Korean city of Buncheon, some 10,000 cameras originally installed in public places to fight crime are feeding a “smart epidemiological investigation system” that will track individuals who have tested positive for infectious diseases, scheduled to begin operation in January 2022, Hankyoreh reported. The city of Ansen is building a system that will alert police when it recognizes emotional expressions that might signal child abuse, scheduled to roll out nationwide in 2023. Given what is known about the efficacy of AI systems that recognize emotional expressions, never mind the identity of a face, such projects demand the highest scrutiny. We’re thinking: Face recognition is a valuable tool in criminal justice, national security, and reunifying trafficked children with their families. Nonetheless, the public has legitimate concerns that such technology invites overreach by governments and commercial interests. In any case, disseminating personal data without consent — and possibly illegally — can only erode the public’s trust in AI systems. Long-Haul ChatbotState-of-the-art chatbots typically are trained on short dialogs. Consequently they often respond with off-point statements in extended conversations. To improve that performance, researchers developed a way to track context throughout a conversation. What's new: Jing Xu, Arthur Szlam, and Jason Weston at Facebook released a chatbot that summarizes dialog on the fly and uses the summary to generate further repartee. Key insight: Chatbots based on the transformer architecture typically generate replies by analyzing up to 1,024 of the most recent tokens (usually characters, words, or portions of words). Facebook previously used a separate transformer to determine which earlier statements were most relevant to a particular reply — but in long conversations, the relevant statements may encompass more than 1,024 tokens. Summarizing such information can give a model access to more context than is available to even large, open-domain chatbots like BlenderBot, Meena, and BART. How it works: The authors built a dataset of over 5,000 conversations. They trained a system of three transformers respectively to summarize conversations as they occurred, select the five summaries most relevant to the latest back-and-forth turn, and generate a response.
Results: Human evaluators compared the authors’ model to a garden-variety BlenderBot, which draws context from the most recent 128 tokens. They scored the authors’ model an average 3.65 out of 5 compared with the BlenderBot’s 3.47. They found 62.1 percent of its responses engaging versus 56.5 percent of the BlenderBot’s responses. Why it matters: After much work on enabling chatbots to discuss a variety of topics, it’s good to see improvement in their ability to converse at length. Conversation is inherently dynamic, and if we want chatbots to keep up with us, we need them to ride a train of thought, hop off the line, switch to a new rail, and shift back to the first — all without losing track. We're thinking: If Facebook were to use this system to generate chatter on the social network, could we call its output Meta data? (Hat tip to Carol-Jean Wu!). A MESSAGE FROM DEEPLEARNING.AICheck out the Generative Adversarial Networks Specialization! Created by leading experts, this specialization will equip you with the foundational knowledge and hands-on training you need to build powerful GANs. Enroll now
Deep Learning for Deep FryingA robot cook is frying to order in fast-food restaurants. Hot off the grill: Flippy 2, a robotic fry station from California-based Miso Robotics, has been newly deployed in a Chicago White Castle location. It operates without a human in the loop to boost throughput, reduce contamination, and perform tasks traditionally allotted to low-paid workers. Special sauce: The robot’s arm slides on an overhead rail. It grabs baskets of raw french fries, chicken wings, onion rings, or what have you, places them in boiling oil, and unloads the finished product — fried to automated perfection — into a chute that conveys cooked food into trays.
A chef’s tale: Flippy 2’s arm pivoted from grilling hamburgers to deep frying. In 2018, its bulkier predecessor’s first job was flipping patties at a Pasadena, California, branch of the CaliBurger chain (owned by CaliGroup, which also owns Miso Robotics). It was taken out of service the next day owing to a crush of novelty-seeking patrons and difficulty placing cooked burgers on a tray, which prompted retraining. Nonetheless, Miso’s emphasis appears to have shifted to frying, and the machine went on to prepare chicken tenders and tater tots at Dodger Stadium, and later french fries and onion rings at White Castle. Why It Matters: Fast food’s high-output, repetitive tasks are well suited to automation. The work can be hot, grueling, and low-wage, leading to turnover of employees that approaches 100 percent annually. Fast-food restaurants in the U.S. are experiencing a wave of walkouts as workers seek higher wages and better working conditions. Robots might pick up the slack — for better or worse. Food for thought: We’ve seen several robotics companies take off as labor shortages related to the pandemic have stoked demand in restaurants and logistics. While the machines will help feed hungry patrons, they’ll also make it harder for humans to get jobs. Companies, institutions, and governments need to establish programs to train displaced employees for jobs that humans are likely to retain. U.S. AI Strategy In GearAn independent commission charged with helping the United States prepare for an era of AI-enabled warfare disbanded last month. Many of its recommendations already are being implemented. What’s new: Wired examined the legacy of the National Security Commission. Its recommendations have been enshrined in over 190 laws this year alone. What they accomplished: The commission, whose 15 members were drawn from government, academia and industry (including executives from Amazon, Google, and Oracle), was founded in 2018 and delivered its final report earlier this year. It took a broad view that emphasized nurturing AI talent and fostering international cooperation (under U.S. leadership). It also recommended integrating AI into regular military operations, using the technology to drive intelligence gathering and analysis, and developing fully autonomous weapons (for use only when authorized by commanders in accordance with international law).
Yes, but: Critics argue the commission’s promotion of military AI could drive an arms race akin to the one that led the U.S. and Russia to stockpile tens of thousands of nuclear weapons between the 1940s and 1990s. They say that the group’s adversarial stance toward geopolitical competitors could further degrade global stability. Others warn that the resulting relationship between the military and private companies could incentivize conflict. Why It matters: Technology is moving fast, and the U.S. has lagged other national efforts to keep pace. A defense-focused roadmap is an important step, yet it invites questions about the nation’s ambitions and values. We’re thinking: The commission’s emphases on accountability, cultivating AI talent, collaborating with allies, and using AI to uphold democratic principles are laudable. At the same time, it raises difficult questions about how to uphold national security in the face of disruptive technologies. We oppose fully autonomous weapons and encourage every member of the AI community to work toward a peaceful, prosperous future that benefits people throughout the world. Work With Andrew Ng
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