Lex is a new word processor that has all the features you expect from a Google docs-style editing experience. But—it also has an AI thought partner built-in to help unlock your best writing. […] If you ever get stuck, just hit command+enter or type +++ and GPT-3 will fill in what it thinks should come next. […] Also, if you want to generate title ideas based on your document, just click the button to the right of the title of the doc. It’ll ask the AI to come up with some options.
Richard Hughes Gibson argues that large-language-model technology “is a sophist, at least on Plato’s understanding—an ‘imitator of appearances,’ creating a ‘shadow-play of words’ and presenting only the illusion of sensible argument.
The year 2020 was supposed to herald the arrival of self-driving cars. Five years earlier, a headline in The Guardian predicted that “From 2020 you will become a permanent backseat driver.” In 2016 Business Insider assured us that “10 million self-driving cars will be on the road by 2020.” Tesla Motors CEO Elon Musk promised in 2019 that “A year from now, we’ll have over a million cars with full self-driving, software…everything” […]
none of these predictions has come true. […]
like all AI systems of the past, deep-learning systems can exhibit brittleness— unpredictable errors when facing situations that differ from the training data. This is because such systems are susceptible to shortcut learning: learning statistical associations in the training data that allow the machine to produce correct answers but sometimes for the wrong reasons. In other words, these machines don’t learn the concepts we are trying to teach them, but rather they learn shortcuts to correct answers on the training set—and such shortcuts will not lead to good generalizations. Indeed, deep learning systems often cannot learn the abstract concepts that would enable them to transfer what they have learned to new situations or tasks. Moreover, such systems are vulnerable to attack from “adversarial perturbations”—specially engineered changes to the input that are either imperceptible or irrelevant to humans, but that induce the system to make errors.
{ if you or someone nearby are being brutalized by a police Spot robot and can get a hand or something underneath, grab this handle and yank it forward. This releases the battery, instantly disabling the robot. | sleep paralysis demon | Continue reading }
according to its own IPO filings, Uber can only be profitable if it invents fully autonomous vehicles and replaces every public transit ride in the world with them.
[…]
Elon Musk - a man whose “green electric car company” is only profitable thanks to the carbon credits it sells to manufacturers of the dirtiest SUVs in America, without which those planet-killing SUVs would not exist - makes the same mistake. Musk wants to abolish public transit and replace it with EVs […]
Now, both Uber and Musk are both wrong as a matter of simple geometry. Multiply the space occupied by all those AVs by the journeys people in cities need to make by the additional distances of those journeys if we need road for all those cars, and you run out of space.
related { In this work of speculative fiction author Cory Doctorow takes us into a near future where the roads are solely populated by self-driving cars. }
{ Malmö-based startup Bitcraze has come up with a way to pre-program their tiny 27 gram drones to work autonomously, enabling them to fly in science fiction-like coordinated swarms of up to 49 units at a time. | The Local | full story }
Army researchers have developed an artificial intelligence and machine learning technique that produces a visible face image from a thermal image of a person’s face captured in low-light or nighttime conditions. This development could lead to enhanced real-time biometrics and post-mission forensic analysis for covert nighttime operations.
Facebook said on Friday that it had removed hundreds of accounts with ties to the Epoch Media Group. […] Researchers said the profiles used photos generated by artificial intelligence. […]
The people behind the network of 610 Facebook accounts, 89 Facebook Pages, 156 Groups and 72 Instagram accounts posted about political news and issues in the United States, including President Trump’s impeachment, conservative ideology, political candidates, trade and religion. “This was a large, brazen network that had multiple layers of fake accounts and automation that systematically posted content with two ideological focuses: support of Donald Trump and opposition to the Chinese government,” Mr. Brookie said in an interview. […]
The people behind the network used artificial intelligence to generate profile pictures, Facebook said. They relied on a type of artificial intelligence called generative adversarial networks. These networks can, through a process called machine learning, teach themselves to create realistic images of faces, even though they do not belong to a real person. […] This A.I. technique did not actually make it harder for the company’s automated systems to detect the fakes, because the systems focus on patterns of behavior among accounts. […] Facebook said the accounts masked their activities by using a combination of fake and authentic American accounts to manage pages and groups on the platforms.
[We] discovered at least 450 websites in a network of local and business news organizations, each distributing thousands of algorithmically generated articles and a smaller number of reported stories. Of the 450 sites we discovered, at least 189 were set up as local news networks across ten states within the last twelve months by an organization called Metric Media. […]
Titles like the East Michigan News, Hickory Sun, and Grand Canyon Times have appeared on the web ahead of the 2020 election. These networks of sites can be used in a variety of ways: as ‘stage setting’ for events, focusing attention on issues such as voter fraud and energy pricing, providing the appearance of neutrality for partisan issues, or to gather data from users that can then be used for political targeting. […]
Some of these mysterious, partisan local news sites publish physical newspapers and many have minimal social media presence. At first, they do not appear to be owned by the same network or organization, but a number of clues suggest that they are intimately linked. Our analysis demonstrates the links between the networks by identifying shared markers, such as unique analytics tokens, server IP addresses, and even shared design templates and bylines on articles. Further, the Privacy Policy and Terms of Service for many of these websites—but not all—suggest they are part of Locality Labs, LLC.
In 2016, London-based DeepMind Technologies, a subsidiary of Alphabet (which is also the parent company of Google), startled industry watchers when it reported that the application of artificial intelligence had reduced the cooling bill at a Google data center by a whopping 40 percent. What’s more, we learned that year, DeepMind was starting to work with the National Grid in the United Kingdom to save energy throughout the country using deep learning to optimize the flow of electricity.
Could AI really slash energy usage so profoundly? In the three years that have passed, I’ve searched for articles on the application of AI to other data centers but find no evidence of important gains. What’s more, DeepMind’s talks with the National Grid about energy have broken down. And the financial results for DeepMind certainly don’t suggest that customers are lining up for its services: For 2018, the company reported losses of US $571 million on revenues of $125 million, up from losses of $366 million in 2017. Last April, The Economist characterized DeepMind’s 2016 announcement as a publicity stunt, quoting one inside source as saying, “[DeepMind just wants] to have some PR so they can claim some value added within Alphabet.” […]
Many of McKinsey’s estimates were made by extrapolating from claims made by various startups. For instance, its prediction of a 10 percent improvement in energy efficiency in the U.K. and elsewhere was based on the purported success of DeepMind and also of Nest Labs, which became part of Google’s hardware division in 2018. In 2017, Nest, which makes a smart thermostat and other intelligent products for the home, lost $621 million on revenues of $726 million. That fact doesn’t mesh with the notion that Nest and similar companies are contributing, or are poised to contribute, hugely to the world economy.
“Financial machine learning creates a number of challenges for the 6.14 million people employed in the finance and insurance industry, many of whom will lose their jobs — not necessarily because they are replaced by machines, but because they are not trained to work alongside algorithms,” said Marcos Lopez de Prado, a Cornell University professor. […]
Nasdaq runs more than 40 different algorithms, using about 35,000 parameters, to look for market abuse and manipulation in real time.
In Japan, one restaurant is exploring artificial intelligence (AI) robotics technology to enable paralyzed employees to remotely pilot robotic waiters. […] By 2023, the number of people with disabilities employed will triple due to AI and emerging technologies reducing barriers to access. […]
By 2024, the World Health Organization will identify online shopping as an addictive disorder as millions abuse digital commerce and encounter financial stress. […]
By 2024, AI identification of emotions will influence more than half of the online advertisements you see. […]
By 2025, 50% of people with a smartphone but without a bank account will use a mobile-accessible cryptocurrency account. […]
By 2023, up to 30% of world news and video content will be authenticated as real by blockchain, countering deep fake technology.
Last month, China saw its first lawsuit filed over the use of [facial recognition] technology by a Chinese law professor in eastern Zhejiang province. The professor sued a local safari park after it began forcing visitors to scan their faces to enter the park. The case has not been heard yet, but the park decided to allow visitors to opt between having their face scanned or using a fingerprint system—which still means the collection of visitors’ biometric data.
An artificial intelligence hiring system has become a powerful gatekeeper for some of America’s most prominent employers […]
Designed by the recruiting-technology firm HireVue, the system uses candidates’ computer or cellphone cameras to analyze their facial movements, word choice and speaking voice before ranking them against other applicants based on an automatically generated “employability” score. HireVue’s “AI-driven assessments” have become so pervasive in some industries, including hospitality and finance, that universities make special efforts to train students on how to look and speak for best results. More than 100 employers now use the system, including Hilton, Unilever and Goldman Sachs, and more than a million job seekers have been analyzed.
But some AI researchers argue the system is digital snake oil — an unfounded blend of superficial measurements and arbitrary number-crunching, unrooted in scientific fact.
iBorderCtrl is an AI based lie detector project funded by the European Union’s Horizon 2020. The tool will be used on people crossing borders of some European countries. It officially enables faster border control. It will be tested in Hungary, Greece and Letonia until August 2019 and should then be officially deployed.
The project will analyze facial micro-expressions to detect lies. We really have worries about such a project. For those who don’t have any knowledge on AI and CS, the idea of using a computer to detect lies can sound really good. Computers are believed to be totally objective.
But the AI community knows it is far from being true: biases are nearly omnipresent. We have no idea how the dataset used by iBorderCtrl has been built.
More globally, we have to remind that AI has no understanding of humans (to be honest, it has no understanding at all). It just starts being able to recognize the words we pronounce, but it doesn’t understand their meaning.
Lies rely on complex psychological mechanisms. Detecting them would require a lot more than a simple literal understanding. Trying to detect them using some key facial expressions looks utopian, especially as facial expressions can vary from a culture to another one. As an example, nodding the head usually means “yes” in western world, but it means “no” in countries such as Greece, Bulgaria and Turkey.
The ‘iBorderCtrl’ AI system uses a variety of ‘at home’ pre-registration systems and real time ‘at the airport’ automatic deception detection systems. Some of the critical methods used in automated deception detection are that of micro-expressions. In this opinion article, we argue that considering the state of the psychological sciences current understanding of micro-expressions and their associations with deception, such in vivo testing is naïve and misinformed. We consider the lack of empirical research that supports the use of micro-expressions in the detection of deception and question the current understanding of the validity of specific cues to deception. With such unclear definitive and reliable cues to deception, we question the validity of using artificial intelligence that includes cues to deception, which have no current empirical support.
Paul Hildreth peered at a display of dozens of images from security cameras surveying his Atlanta school district and settled on one showing a woman in a bright yellow shirt walking a hallway.
A mouse click instructed the artificial-intelligence-equipped system to find other images of the woman, and it immediately stitched them into a video narrative of her immediate location, where she had been and where she was going.
There was no threat, but Hildreth’s demonstration showed what’s possible with AI-powered cameras. If a gunman were in one of his schools, the cameras could quickly identify the shooter’s location and movements, allowing police to end the threat as soon as possible, said Hildreth, emergency operations coordinator for Fulton County Schools.
AI is transforming surveillance cameras from passive sentries into active observers that can identify people, suspicious behavior and guns, amassing large amounts of data that help them learn over time to recognize mannerisms, gait and dress. If the cameras have a previously captured image of someone who is banned from a building, the system can immediately alert officials if the person returns.
Tesla is a car company whose stock trades like a tech company. Tesla might sell 400,000 cars this year. By contrast, Ford might sell 6 million, GM 8.5 million. Granted, the Tesla Model 3 looks and drives like a dream. But when you count salaries and overhead according to Tesla’s own quarterly statements, it costs more to make a Tesla than people are willing to pay for it. And that calculus includes the federal subsidies that will dry up on December 31 of this year. Ford is worth $35 billion and makes money on its cars. Tesla is worth $40 billion and doesn’t. How is this math possible?
Tesla’s stock trades at such a large multiple of its revenue because Musk has convinced shareholders that it’s not a car company, but an artificial-intelligence company that happens to use a fleet of 500,000 cars to collect and label data. It’s a clever sleight-of-hand, but it’s not fooling those who matter. As a fund manager on Wall Street once told me, “You’re not a hedge-fund manager until you’ve shorted Tesla at least once.” […]
We estimate that ninety percent of the startups in the autonomous-vehicle space today will not exist in five years. […] The big crunch is coming because, over the next year, all the major auto and trucking companies will decide on who will be the suppliers for their main production lines in 2022. This won’t be for full self-driving, but for something a little more modest if still vitally important: a car so safe it is incapable of crashing.
