Last year the artificial intelligence group DeepMind cracked a mystery that has flummoxed scientists for decades: stripping bare the structure of proteins, the building blocks of life. Now, having amassed a database of nearly all human protein structures, the company is making the resource available online free for researchers to use. From a report: The key to understanding our basic biological machinery is its architecture. The chains of amino acids that comprise proteins twist and turn to make the most confounding of 3D shapes. It is this elaborate form that explains protein function; from enzymes that are crucial to metabolism to antibodies that fight infectious attacks. Despite years of onerous and expensive lab work that began in the 1950s, scientists have only decoded the structure of a fraction of human proteins. DeepMind's AI program, AlphaFold, has predicted the structure of nearly all 20,000 proteins expressed by humans. In an independent benchmark test that compared predictions to known structures, the system was able to predict the shape of a protein to a good standard 95% of time. DeepMind, which has partnered with the European Molecular Biology Laboratory's European Bioinformatics Institute (EMBL-EBI), hopes the database will help researchers to analyse how life works at an atomic scale by unpacking the apparatus that drives some diseases, make strides in the field of personalised medicine, create more nutritious crops and develop "green enzymes" that can break down plastic.

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OpenAI has disbanded its robotics team after years of research into machines that can learn to perform tasks like solving a Rubik's Cube. Company cofounder Wojciech Zaremba quietly revealed on a podcast hosted by startup Weights & Biases that OpenAI has shifted its focus to other domains, where data is more readily available. From a report: "So it turns out that we can make a gigantic progress whenever we have access to data. And I kept all of our machinery unsupervised, [using] reinforcement learning -- [it] work[s] extremely well. There [are] actually plenty of domains that are very, very rich with data. And ultimately that was holding us back in terms of robotics," Zaremba said. "The decision [to disband the robotics team] was quite hard for me. But I got the realization some time ago that actually, that's for the best from the perspective of the company." In a statement, an OpenAI spokesperson told VentureBeat: "After advancing the state of the art in reinforcement learning through our Rubik's Cube project and other initiatives, last October we decided not to pursue further robotics research and instead refocus the team on other projects. Because of the rapid progress in AI and its capabilities, we've found that other approaches, such as reinforcement learning with human feedback, lead to faster progress in our reinforcement learning research."

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A new documentary film has harnessed artificial intelligence to artificially voice quotes from its subject, the late Anthony Bourdain. From a report: Details of the dubious decision are outlined in a piece in The New Yorker, and raise a heap of uncomfortable questions about whether or not it's ethical to put words in the mouths of the deceased, whether or not they penned them during their life. The lines appear in filmmaker Morgan Neville's new documentary, Roadrunner, when an email from Bourdain is initially read by the recipient, but the audio then transitions into Bourdain's own voice.

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An anonymous reader quotes a report from Motherboard: With only 30 minutes of audio, companies can now create a digital clone of your voice and make it say words you never said. Using machine learning, voice AI companies like VocaliD can create synthetic voices from a person's recorded speech -- adopting unique qualities like speaking rhythm, pronunciation of consonants and vowels, and intonation. For tech companies, the ability to generate any sentence with a realistic-sounding human voice is an exciting, cost-saving frontier. But for the voice actors whose recordings form the foundation of text-to-speech (TTS) voices, this technology threatens to disrupt their livelihoods, raising questions about fair compensation and human agency in the age of AI. At the center of this reckoning is voice actress Bev Standing, who is suing TikTok after alleging the company used her voice for its text-to-speech feature without compensation or consent. This is not the first case like this; voice actress Susan Bennett discovered that audio she recorded for another company was repurposed to be the voice of Siri after Apple launched the feature in 2011. She was paid for the initial recording session but not for being Siri. Rallying behind Standing, voice actors donated to a GoFundMe that has raised nearly $7,000 towards her legal expenses and posted TikTok videos under the #StandingWithBev hashtag warning users about the feature. Standing's supporters say the TikTok lawsuit is not just about Standing's voice -- it's about the future of an entire industry attempting to adapt to new advancements in the field of machine learning. Standing's case materializes some performers' worst fears about the control this technology gives companies over their voices. Her lawsuit claims TikTok did not pay or notify her to use her likeness for its text-to-speech feature, and that some videos using it voiced "foul and offensive language" causing "irreparable harm" to her reputation. Brands advertising on TikTok also had the text-to-speech voice at their disposal, meaning her voice could be used for explicitly commercial purposes. [...] Laws protecting individuals from unauthorized clones of their voices are also in their infancy. Standing's lawsuit invokes her right of publicity, which grants individuals the right to control commercial uses of their likeness, including their voice. In November 2020, New York became the first state to apply this right to digital replicas after years of advocacy from SAG-AFTRA, a performers' union. "We look to make sure that state rights of publicity are as strong as they can be, that any limitations on people being able to protect their image and voice are very narrowly drawn on first amendment lines," Jeffrey Bennett, a general counsel for SAG-AFTRA, told Motherboard. "We look at this as a potentially great right of publicity case for this voice professional whose voice is being used in a commercial manner without her consent."

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An anonymous reader quotes a report from BuzzFeed News: A government inquiry into federal agencies' deployment of facial recognition may have overlooked some organizations' use of popular biometric identification software Clearview AI, calling into question whether authorities can understand the extent to which the emerging technology has been used by taxpayer-funded entities. In a 92-page report published by the Government Accountability Office on Tuesday, five agencies -- the US Capitol Police, the US Probation Office, the Pentagon Force Protection Agency, Transportation Security Administration, and the Criminal Investigation Division at the Internal Revenue Service -- said they didn't use Clearview AI between April 2018 and March 2020. This, however, contradicts internal Clearview data previously reviewed by BuzzFeed News. In April, BuzzFeed News revealed that those five agencies were among more than 1,800 US taxpayer-funded entities that had employees who tried or used Clearview AI, based on internal company data. As part of that story, BuzzFeed News published a searchable table disclosing all the federal, state, and city government organizations whose employees are listed in the data as having used the facial recognition software as of February 2020. While the GAO was tasked with "review[ing] federal law enforcement use of facial recognition technology," the discrepancies between the report, which was based on survey responses and BuzzFeed News' past reporting, suggest that even the US government may not be equipped to track how its own agencies access to surveillance tools like Clearview. The GAO report surveyed 42 federal agencies in total, 20 of which reported that they either owned their own facial recognition system or used one developed by a third party between April 2018 and March 2020. Ten federal agencies -- including Immigration and Customs Enforcement and Customs and Border Protection -- said they specifically used Clearview AI.

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The state of Maine now has the most stringent laws regulating government use of facial recognition in the country. The Verge reports: The new law prohibits government use of facial recognition except in specifically outlined situations, with the most broad exception being if police have probable cause that an unidentified person in an image committed a serious crime, or for proactive fraud prevention. Since Maine police will not have access to facial recognition, they will be able to ask the FBI and Maine Bureau of Motor Vehicles (BMV) to run these searches. Crucially, the law plugs loopholes that police have used in the past to gain access to the technology, like informally asking other agencies or third parties to run backchannel searches for them. Logs of all facial recognition searches by the BMV must be created and are designated as public records. The only other state-wide facial recognition law was enacted by Washington in 2020, but many privacy advocates were dissatisfied with the specifics of the law. Maine's new law also gives citizens the ability to sue the state if they've been unlawfully targeted by facial recognition, which was notably absent from Washington's regulation. If facial recognition searches are performed illegally, they must be deleted and cannot be used as evidence. In response to this new law, the ACLU said: "Maine is showing the rest of the country what it looks like when we the people are in control of our civil rights and civil liberties, not tech companies that stand to profit from widespread government use of face surveillance technology."

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Artificial intelligence is to be used to tackle the most deadly parasitic diseases in the developing world, tech company DeepMind has announced. The BBC reports: The London-based Alphabet-owned lab will work with the Drugs for Neglected Diseases initiative (DNDI) to treat Chagas disease and Leishmaniasis. Scientists spend years in laboratories mapping protein structures. But last year, DeepMind's AlphaFold program was able to achieve the same accuracy in a matter of days. Many diseases are linked to the roles of proteins in: catalysing chemical reactions (enzymes); fighting disease (antibodies); and acting as chemical messengers (hormones such as insulin). And knowing the 3D structure of a protein is important in developing treatments for, among others, cancer, dementia and infectious diseases. Prof Dame Janet Thornton, of the European Bioinformatics Institute, told BBC News: "Most new drugs in recent years have been developed using protein-structural data as one part of the process. "There are, however, many other aspects which need to be taken into account, which, due to lack of data, may not be amenable to AI approaches." But the predictions would be "particularly valuable" for pathogens with unknown protein structures, including some neglected diseases. "Developing new AI approaches for designing such drugs is a new challenge but one to which the new AI techniques can be applied and this holds out great hope for the future," Dame Janet added.

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This may sound like something straight out of a sci-fi movie, but Canon has rolled out new AI cameras that use "smile recognition" technology to ensure that only happy employees are allowed into its offices. From a report: Back in 2020, the China-based Canon subsidiary Canon Information Technology introduced an "intelligent IT solution" for corporate offices that includes 5 different functional modules, one of which is "smiley face access control. In addition, based on the corporate culture of 'moving and always being,' Canon has always advocated the concepts of 'laughing' and 'big health,' and hopes to bring happiness and health to everyone in the post-epidemic era," Canon wrote in a press release. "Therefore, in the [...] intelligent IT solution, a new experience of smile recognition is specially incorporated. It is hoped that smiles can let everyone relax and get healthy, so as to create a more pleasant working atmosphere and improve efficiency."

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Check out this video footage of the sleek Mayflower 400 slicing through the water, hoping to retrace the historic 1620 journey of the famous ship which carried pilgrims to America. Unfortunately, unlike the real Mayflower, this robotic 21st-century doppelganger "had to turn back Friday to fix a mechanical problem," reports the Associated Press: Nonprofit marine research organization ProMare, which worked with IBM to build the autonomous ship, said it made the decision to return to base "to investigate and fix a minor mechanical issue" but hopes to be back on the trans-Atlantic journey as soon as possible. With no humans on board the ship, there's no one to make repairs while it's at sea. Piloted by artificial intelligence technology, the 50-foot (15-meter) Mayflower Autonomous Ship began its trip early Tuesday, departing from Plymouth, England, and spending some time off the Isles of Scilly before it headed for deeper waters. It was supposed to take up to three weeks to reach Provincetown on Cape Cod before making its way to Plymouth, Massachusetts. If successful, it would be the largest autonomous vessel to cross the Atlantic.

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An anonymous reader shares a report: Google CEO Sundar Pichai last month previewed an artificial intelligence model that he said would enable people to have open-ended conversations with technology. But current and former employees who have worked with the language model say enabling coherent, free-flowing and accurate dialogue between humans and technology remains a tall order. As a result, Google is taking a more incremental step in conversational AI by preparing to teach foreign languages through Google Search [Editor's note: the link may be paywalled; alternative source], according to people involved in the work. The project, referred to internally as Tivoli, grew out of its Google Research unit and is likely to be rolled out later this year. It will initially work over text, and the exact look and feel of the instruction couldn't be learned. Googlers are also discussing ways to eventually add the functionality to its voice assistant and YouTube product lines. In YouTube, for example, it could generate language quizzes where viewers record themselves after watching a video and the AI provides an assessment of how they performed. A Google spokesperson did not have a comment. Teaching foreign languages allows Google to move more fluid, conversational AI beyond silly exchanges to a practical-use but low-stakes case, the people said. Using the wrong tense or phrase would be unlikely to cause serious harm to users. AI researchers have for decades worked to foster dialogue between computers and humans that feels real, picks up the nuance of how people communicate and simplifies tasks. Such aspirational technology has been featured in movies like "Her" in which a man communicates with -- and falls in love with -- a virtual assistant.

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McDonald's equiped 10 of its restaurants in Chicago with automated speech-recognition for their drive-through windows. Now they're facing a potential class-action lawsuit. Long-time Slashdot reader KindMind shares this report from the Register: McDonald's has been accused of illegally collecting and processing customers' voice recordings without their consent in the U.S. state of Illinois... The state has some of the strictest data privacy laws; its Biometric Information Privacy Act (BIPA) states: "No private entity may collect, capture, purchase, receive through trade, or otherwise obtain a person's or a customer's biometric identifier or biometric information." unless it receives written consent. Shannon Carpenter, a resident of Illinois, sued [PDF] McDonald's in April on behalf of himself and all other affected state residents. He claimed the fast-chow biz has broken BIPA by not obtaining written consent from its customers to collect and process their voice data, nor has it explained in its privacy policy how or if the data is stored or deleted. His lawsuit also stated that McDonald's has been experimenting with AI software taking orders at its drive thrus since last year. "Plaintiff, like the other class members, to this day does not know the whereabouts of his voiceprint biometrics which defendant obtained," Carpenter's lawsuit stated. Under the BIPA, people can receive up to $5,000 in damages from private entities for each violation committed "intentionally or recklessly," or $1,000 if each violation was from negligence instead. The suit also claimed the machine-learning software built by McD Tech Labs doesn't just transcribe speech into text, it processes audio samples to glean all sorts of personal information to predict a customer's "age, gender, accent, nationality, and national origin."

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An anonymous reader quotes a report from The Wall Street Journal: The Biden administration launched an initiative Thursday aiming to make more government data available to artificial intelligence researchers, part of a broader push to keep the U.S. on the cutting edge of the crucial new technology. The National Artificial Intelligence Research Resource Task Force, a group of 12 members from academia, government, and industry led by officials from the White House Office of Science and Technology Policy and the National Science Foundation, will draft a strategy for potentially giving researchers access to stores of data about Americans, from demographics to health and driving habits. They would also look to make available computing power to analyze the data, with the goal of allowing access to researchers across the country. The task force, which Congress mandated in the National Artificial Intelligence Initiative Act of 2020, is part of an effort across the government to ensure the U.S. remains at the vanguard of technological advancements. Many researchers, particularly in academia, simply don't have access to these computational resources and data, and this is hampering innovation. One example: The Transportation Department has access to a set of data gathered from vehicle sensors about how people drive, said Erwin Gianchandani, senior adviser at the National Science Foundation and co-chairman of the new AI task force. "Because you have very sensitive data about individuals, there are challenges in being able to make that data available to the broader research community," he said. On the other hand, if researchers could get access, they could develop innovations designed to make driving safer. Census data, medical records, and other data sets could also potentially be made available for research by both private companies and academic institutions, officials said. They said the task force will evaluate how to make such data available while protecting Americans' privacy and addressing other ethical concerns.

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An anonymous reader shares an excerpt from an interview The Guardian conducted with Microsoft's Kate Crawford. "Kate Crawford studies the social and political implications of artificial intelligence," writes Zoe Corbyn via The Guardian. "She is a research professor of communication and science and technology studies at the University of Southern California and a senior principal researcher at Microsoft Research. Her new book, Atlas of AI, looks at what it takes to make AI and what's at stake as it reshapes our world." Here's an excerpt from the interview: What should people know about how AI products are made? We aren't used to thinking about these systems in terms of the environmental costs. But saying, "Hey, Alexa, order me some toilet rolls," invokes into being this chain of extraction, which goes all around the planet... We've got a long way to go before this is green technology. Also, systems might seem automated but when we pull away the curtain we see large amounts of low paid labour, everything from crowd work categorizing data to the never-ending toil of shuffling Amazon boxes. AI is neither artificial nor intelligent. It is made from natural resources and it is people who are performing the tasks to make the systems appear autonomous. Problems of bias have been well documented in AI technology. Can more data solve that? Bias is too narrow a term for the sorts of problems we're talking about. Time and again, we see these systems producing errors -- women offered less credit by credit-worthiness algorithms, black faces mislabelled -- and the response has been: "We just need more data." But I've tried to look at these deeper logics of classification and you start to see forms of discrimination, not just when systems are applied, but in how they are built and trained to see the world. Training datasets used for machine learning software that casually categorize people into just one of two genders; that label people according to their skin color into one of five racial categories, and which attempt, based on how people look, to assign moral or ethical character. The idea that you can make these determinations based on appearance has a dark past and unfortunately the politics of classification has become baked into the substrates of AI. What do you mean when you say we need to focus less on the ethics of AI and more on power? Ethics are necessary, but not sufficient. More helpful are questions such as, who benefits and who is harmed by this AI system? And does it put power in the hands of the already powerful? What we see time and again, from facial recognition to tracking and surveillance in workplaces, is these systems are empowering already powerful institutions -- corporations, militaries and police. What's needed to make things better? Much stronger regulatory regimes and greater rigour and responsibility around how training datasets are constructed. We also need different voices in these debates -- including people who are seeing and living with the downsides of these systems. And we need a renewed politics of refusal that challenges the narrative that just because a technology can be built it should be deployed.

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"For the first time, artificial intelligence was a key component and power multiplier in fighting the enemy," says a senior officer in the intelligence corps of the Israeli military, describing the technology's use in 11 days of fighting in the Gaza Strip. They're quoted in a Jerusalem Post article on "the world's first AI war": Soldiers in Unit 8200, an Intelligence Corps elite unit, pioneered algorithms and code that led to several new programs called "Alchemist," "Gospel" and "Depth of Wisdom," which were developed and used during the fighting. Collecting data using signal intelligence, visual intelligence, human intelligence , geographical intelligence, and more, the Israel Defense Forces (IDF) has mountains of raw data that must be combed through to find the key pieces necessary to carry out a strike. "Gospel" used AI to generate recommendations for troops in the research division of Military Intelligence, which used them to produce quality targets and then passed them on to the IAF to strike... While the IDF had gathered thousands of targets in the densely populated coastal enclave over the past two years, hundreds were gathered in real time, including missile launchers that were aimed at Tel Aviv and Jerusalem. The military believes using AI helped shorten the length of the fighting, having been effective and quick in gathering targets using super-cognition. The IDF carried out hundreds of strikes against Hamas and PIJ, including rocket launchers, rocket manufacturing, production and storage sites, military intelligence offices, drones, commanders' residences and Hamas's naval commando unit. Israel has destroyed most of the naval commando unit's infrastructure and weaponry, including several autonomous GPS-guided submarines that can carry 30 kg. of explosives. IDF Unit 9900's satellites have gathered geographical intelligence over the years. They were able to automatically detect changes in terrain in real time so that during the operation, the military was able to detect launching positions and hit them after firing. For example, Unit 9900 troops using satellite imagery were able to detect 14 rocket launchers that were located next to a school... One strike, against senior Hamas operative Bassem Issa, was carried out with no civilian casualties despite being in a tunnel under a high-rise building surrounded by six schools and a medical clinic... Hamas's underground "Metro" tunnel network was also heavily damaged over the course of several nights of airstrikes. Military sources said they were able to map the network, consisting of hundreds of kilometers under residential areas, to a degree where they knew almost everything about them. The mapping of Hamas's underground network was done by a massive intelligence-gathering process that was helped by the technological developments and use of Big Data to fuse all the intelligence.

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A Canadian startup called Listing AI is using AI to quickly churn out computer-generated descriptions of real estate. All users need to do is give it some details about the home, and the AI does the rest. CNN reports: "L O V E L Y Oakland!" the house description began. It went on to give a slew of details about the 1,484 square-foot home -- light-filled, charming, Mediterranean-style, with a yard that "boasts lush front landscaping" -- and finished by describing the "cozy fireplace" and "rustic-chic" pressed tin ceiling in the living room. The results still need work: The real-life Oakland, California, home that fits with the above description (which my family is currently selling) actually has a pressed tin ceiling in the dining room, rather than the living room, for instance. The descriptions Listing AI created for me are not nearly as specific or well-written as the one crafted by our (human) realtor. And I had to provide the website with a lot of information about different rooms and features of the house and the outdoor landscaping -- a process that felt a bit like real-estate Mad Libs -- before the website was able to come up with several different descriptions. But the general coherence of the descriptions that Listing AI proposed within seconds of my submission provides yet another sign that AI is getting better at a task that was traditionally seen as uniquely human -- and shows how people may be able to work with the technology, rather than fearing it may replace us. It probably won't do all the work of writing a house description for you, but according to Listing AI co-founder Mustafa Al-Hayali, that's not the point. He hopes it will complete about 80% to 90% of the work for coming up with a home description, which may be completed by a realtor or a copy writer. "I don't believe it's meant to replace a person when it comes to completing a task, but it's supposed to make their job a whole lot easier," Al-Hayali told CNN Business. "It can generate ideas you can use." The information used in the app is processed by GPT-3, an AI model from nonprofit research company OpenAI. According to MIT Technology Review, GPT-3 could herald a new type of search engine.

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Researchers at Intel Labs have applied machine learning techniques to GTA 5 to make it look incredibly realistic. Gizmodo reports: [I]nstead of training a neural network on famous masterpieces, the researchers at Intel Labs relied on the Cityscapes Dataset, a collection of images of a German city's urban center captured by a car's built-in camera, for training. When a different artistic style is applied to footage using machine learning techniques, the results are often temporally unstable, which means that frame by frame there are weird artifacts jumping around, appearing and reappearing, that diminish how real the results look. With this new approach, the rendered effects exhibit none of those telltale artifacts, because in addition to processing the footage rendered by Grand Theft Auto V's game engine, the neural network also uses other rendered data the game's engine has access to, like the depth of objects in a scene, and information about how the lighting is being processed and rendered. That's a gross simplification -- you can read a more in-depth explanation of the research here -- but the results are remarkably photorealistic. The surface of the road is smoothed out, highlights on vehicles look more pronounced, and the surrounding hills in several clips look more lush and alive with vegetation. What's even more impressive is that the researchers think, with the right hardware and further optimization, the gameplay footage could be enhanced by their convolutional network at "interactive rates" -- another way to say in real-time -- when baked into a video game's rendering engine.

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An anonymous reader quotes a report from The Verge: Amazon's Alexa has a voice familiar to millions: calm, warm, and measured. But like most synthetic speech, its tones have a human origin. There was someone whose voice had to be recorded, analyzed, and algorithmically reproduced to create Alexa as we know it now. Amazon has never revealed who this "original Alexa" is, but journalist Brad Stone says he tracked her down, and she is Nina Rolle, a voiceover artist based in Boulder, Colorado. The claim comes from Stone's upcoming book on the tech giant, Amazon Unbound, an excerpt of which is published here in Wired. Neither Amazon nor Rolle confirmed or denied Stone's reporting, which he says is based on conversations with the professional voiceover community, but Rolle's voice alone makes for a compelling case. Here's how Stone writes up the process in selecting Alexa's voice: "Believing that the selection of the right voice for Alexa was critical, [then-Amazon exec Greg] Hart and colleagues spent months reviewing the recordings of various candidates that GM Voices produced for the project, and presented the top picks to Bezos. The Amazon team ranked the best ones, asked for additional samples, and finally made a choice. Bezos signed off on it. Characteristically secretive, Amazon has never revealed the name of the voice artist behind Alexa. I learned her identity after canvasing the professional voice-over community: Boulder, Colorado -- based voice actress and singer Nina Rolle. Her professional website contains links to old radio ads for products such as Mott's Apple Juice and the Volkswagen Passat -- and the warm timbre of Alexa's voice is unmistakable. Rolle said she wasn't allowed to talk to me when I reached her on the phone in February 2021. When I asked Amazon to speak with her, they declined."

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Long-time Slashdot reader AmiMoJo quotes the Verge's warning about "deepfake geography: AI-generated images of cityscapes and countryside." Specifically, geographers are concerned about the spread of fake, AI-generated satellite imagery. Such pictures could mislead in a variety of ways. They could be used to create hoaxes about wildfires or floods, or to discredit stories based on real satellite imagery... Deepfake geography might even be a national security issue, as geopolitical adversaries use fake satellite imagery to mislead foes... The first step to tackling these issues is to make people aware there's a problem in the first place, says Bo Zhao, an assistant professor of geography at the University of Washington. Zhao and his colleagues recently published a paper on the subject of "deep fake geography," which includes their own experiments generating and detecting this imagery... As part of their study, Zhao and his colleagues created software to generate deepfake satellite images, using the same basic AI method (a technique known as generative adversarial networks, or GANs) used in well-known programs like ThisPersonDoesNotExist.com. They then created detection software that was able to spot the fakes based on characteristics like texture, contrast, and color. But as experts have warned for years regarding deepfakes of people, any detection tool needs constant updates to keep up with improvements in deepfake generation.

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The White House has launched a new website, AI.gov, to make artificial intelligence research more accessible across the nation. Axios: The U.S. once led significantly in the global artificial intelligence race, but now risks being overtaken by China. This is one step the White House is taking to drum up excitement for AI and broaden educational opportunities in the field. The website's target audience is the general public, and its purpose is to make public information available on AI more visible to someone like a teacher or student interested in science. Users will be able to visit the website to learn how artificial intelligence is being used across the nation in a variety of ways, including to respond to the COVID pandemic and weather forecasting, for example. It's also meant to be a tool to advance research.

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"Despite early claims by #Tesla #ElonMusk, Autopilot WAS engaged in tragic crash in The Woodlands," tweeted U.S. Congressman Kevin Brady on Wednesday. (Adding "We need answers.") But maybe it depends on how you define Autopilot. CNN reports: Tesla said Monday that one of Autopilot's features was active during the April 17 crash that killed two men in Spring, Texas.... Lars Moravy, Tesla's vice president of vehicle engineering, said on the company's earnings call Monday that Tesla's adaptive cruise control was engaged and accelerated to 30 mph before the car crashed. Autopilot is a suite of driver assistance features, including traffic-aware cruise control and Autosteer, according to Tesla's website... The North American owner's manuals for the Model 3, Model S and Model X, all describe traffic-aware cruise control as an Autopilot feature. Tesla's revelation may be at odds with the initial description of the crash from its CEO Elon Musk, who said two days after the crash that "data logs recovered so far show Autopilot was not enabled." Alternately, Forbes suggests there may just be some confusion, noting that earnings call included descriptions of tests Tesla performed on one of their own cars after the accident. So when they said adaptive cruise control "only accelerated the car to 30mph [over] the distance before the car crashed," they could just have been referring to their own experiments. (Tesla also points out adaptive cruise control only engages when the driver is buckled — and disengages slowly if they're unbuckled — and after the Texas crash all seat belts were unbuckled.) Why so much confusion? Part of the problem may be, as CNN points out, that Tesla "generally does not engage with the professional news media." But The Drive shares another theory about the crash: A relative of the deceased told a local news station that the owner allegedly "may have hopped in the back seat after backing the car out of the driveway." Moments later, the car crashed when it failed to negotiate a turn at high speed. CNN adds: Bryan Reimer, the associate director of the New England University Transportation Center at MIT, who studies driver assistance systems like Autopilot, said one of the plausible explanations for the crash is that the driver was confused and thought they had activated Autosteer, when only traffic-aware cruise control had been turned on. "The general understanding of Autopilot is that it's one feature, but in reality it is two things bolted together," said Reimer, referring to traffic-aware cruise control and Autosteer. But according to the Washington Post, Tesla also disputes that theory: Tesla executives on Monday claimed a driver was behind the wheel at the time of a fatal crash that killed two in suburban Houston this month, contradicting local authorities who have previously said they were certain no one was in that seat. Tesla made the statement on its earnings call Monday... Lars Moravy, the company's vice president of vehicle engineering, said the steering wheel was "deformed," indicating a driver's presence at the time of the crash... Mark Herman, constable for Harris County Precinct 4, told the station KHOU that police were "100 percent certain that no one was in the driver's seat."

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