The San Francisco police could use robots to deploy lethal force under a policy advanced by city supervisors on Tuesday that thrust the city into the forefront of a national debate about the use of weaponized robots in American cities. The possibility is not merely hypothetical. In 2016, the Dallas Police Department ended a standoff with a gunman suspected of killing five officers by blowing him up with a bomb attached to a robot in what was believed to be the first lethal use of the technology by an American law enforcement agency. Supporters of the policy, advanced by the San Francisco Board of Supervisors by an 8-to-3 vote, said it would allow the police to deploy a robot with deadly force in extraordinary circumstances, such as when a mass shooter or a terrorist is threatening the lives of officers or civilians. David Lazar, assistant chief of the San Francisco Police Department, cited as an example the gunman who opened fire from his Las Vegas high-rise hotel room in 2017, killing 60 people in the deadliest mass shooting in modern American history.

Digital transformation is the future of your organization -- but reading about digital transformation strategies can feel like looking at a bowl of alphabet soup. Studies show that only about 7 percent of corporate leadership is digitally savvy, which means you might feel a bit out of your ken as your organization begins adopting new strategies and processes in the name of digital transformation. Remaining relevant in the modern business environment will require plenty of engagement with digital education. In the meantime, you can use the following glossary of acronyms to help you decipher the memos you receive about your business's ongoing digital transformation. Artificial intelligence (AI) is intelligence demonstrated by machines, as opposed to the natural intelligence displayed by animals including humans.

As Tesla faces a federal investigation and lawsuits over fatal accidents involving its Autopilot system, shaking public confidence in robotic cars, could a pared-down approach like the one described -- variously called "partial autonomy" or "driver assistance" systems -- be the more realistic future of hands-free driving? This type of system, more like a no-nonsense chaperone than one you would find in a fully robotic car, is a necessary component for top scores from the Insurance Institute for Highway Safety's forthcoming ratings of partial-autonomous tech; high ratings from the independent nonprofit are prized. And though General Motors is taking the lead with their Super Cruise system, they not alone; Ford, BMW and Mercedes-Benz are making similar attempts. Super Cruise combines minutely detailed, 3-D laser-scanned roadway maps with cameras, radar and onboard GPS. By the end of this year, the company intends to expand the system's network to two-way highways for the first time and double its total operational domain to 400,000 miles.

Elon Musk has long used his mighty Twitter megaphone to amplify the idea that Tesla's automated driving software isn't just safe -- it's safer than anything a human driver can achieve. That campaign kicked into overdrive last fall when the electric-car maker expanded its Full Self-Driving "beta" program from a few thousand people to a fleet that now numbers more than 100,000. The $12,000 feature purportedly lets a Tesla drive itself on highways and neighborhood streets, changing lanes, making turns and obeying traffic signs and signals. As critics scolded Musk for testing experimental technology on public roads without trained safety drivers as backups, Santa Monica investment manager and vocal Tesla booster Ross Gerber was among the allies who sprang to his defense. "There has not been one accident or injury since FSD beta launch," he tweeted in January.

Teslas with partially automated driving systems are a step closer to being recalled after the U.S. elevated its investigation into a series of collisions with parked emergency vehicles or trucks with warning signs. The National Highway Traffic Safety Administration said Thursday that it is upgrading the Tesla probe to an engineering analysis, another sign of increased scrutiny of the electric vehicle maker and automated systems that perform at least some driving tasks. Documents posted Thursday by the agency raise some serious issues about Tesla's Autopilot system. The agency found that it's being used in areas where its capabilities are limited, and that many drivers aren't taking action to avoid crashes despite warnings from the vehicle. The probe now covers 830,000 vehicles, almost everything that the Austin, Texas, carmaker has sold in the U.S. since the start of the 2014 model year.

If you own a Tesla, or a loved one does, or you're thinking about buying one, or you share public roads with Tesla cars, you might want to watch the new documentary "Elon Musk's Crash Course." Premiering Friday on FX and Hulu, the 75-minute fright show spotlights the persistent dangers of Tesla's automated driving technologies, the company's lax safety culture, Musk's P.T. Barnum-style marketing hype and the weak-kneed safety regulators who seem not to care. Get Screen Gab for weekly recommendations, analysis, interviews and irreverent discussion of the TV and streaming movies everyone's talking about. You may occasionally receive promotional content from the Los Angeles Times. The central through line is the story of Joshua Brown, a rabid Tesla fan and derring-do techno-geek beheaded when his Autopilot-engaged Tesla drove itself at full speed on a Florida highway underneath the trailer of a semi-truck in 2016.

The term"dox" is an abbreviation for"documents," and doxing is the act of disclosing private, sensitive, or personally identifiable information about a person without their consent. Sensitive information can be considered as any type of confidential information or any information that can be used to identify a person uniquely. This information is called doxed information and includes demographic information [53] such as birthday, sexual orientation, race, ethnicity, and religion, or location information which can be used to precisely or approximately locate a person such as the street address, ZIP code, IP address, and GPS coordinates. Other categories of doxed information are identity documents like passport number and social security number, contact information like phone number and email address, financial information such as credit card and bank account details, or sign-in credentials such as usernames and passwords[15]. Such disclosure may have various consequences. It may encourage forms of bigotry and hate groups, encourage human or child trafficking and endanger people's lives or reputations, scare and intimidate people by swatting

Artificial intelligence (AI) has become a part of everyday conversation and our lives. It is considered as the new electricity that is revolutionizing the world. AI is heavily invested in both industry and academy. However, there is also a lot of hype in the current AI debate. AI based on so-called deep learning has achieved impressive results in many problems, but its limits are already visible. AI has been under research since the 1940s, and the industry has seen many ups and downs due to over-expectations and related disappointments that have followed. The purpose of this book is to give a realistic picture of AI, its history, its potential and limitations. We believe that AI is a helper, not a ruler of humans. We begin by describing what AI is and how it has evolved over the decades. After fundamentals, we explain the importance of massive data for the current mainstream of artificial intelligence. The most common representations for AI, methods, and machine learning are covered. In addition, the main application areas are introduced. Computer vision has been central to the development of AI. The book provides a general introduction to computer vision, and includes an exposure to the results and applications of our own research. Emotions are central to human intelligence, but little use has been made in AI. We present the basics of emotional intelligence and our own research on the topic. We discuss super-intelligence that transcends human understanding, explaining why such achievement seems impossible on the basis of present knowledge,and how AI could be improved. Finally, a summary is made of the current state of AI and what to do in the future. In the appendix, we look at the development of AI education, especially from the perspective of contents at our own university.

Classification of malware families is crucial for a comprehensive understanding of how they can infect devices, computers, or systems. Thus, malware identification enables security researchers and incident responders to take precautions against malware and accelerate mitigation. API call sequences made by malware are widely utilized features by machine and deep learning models for malware classification as these sequences represent the behavior of malware. However, traditional machine and deep learning models remain incapable of capturing sequence relationships between API calls. On the other hand, the transformer-based models process sequences as a whole and learn relationships between API calls due to multi-head attention mechanisms and positional embeddings. Our experiments demonstrate that the transformer model with one transformer block layer surpassed the widely used base architecture, LSTM. Moreover, BERT or CANINE, pre-trained transformer models, outperformed in classifying highly imbalanced malware families according to evaluation metrics, F1-score, and AUC score. Furthermore, the proposed bagging-based random transformer forest (RTF), an ensemble of BERT or CANINE, has reached the state-of-the-art evaluation scores on three out of four datasets, particularly state-of-the-art F1-score of 0.6149 on one of the commonly used benchmark dataset.

Modern neural language models widely used in tasks across NLP risk memorizing sensitive information from their training data. As models continue to scale up in parameters, training data, and compute, understanding memorization in language models is both important from a learning-theoretical point of view, and is practically crucial in real world applications. An open question in previous studies of memorization in language models is how to filter out "common" memorization. In fact, most memorization criteria strongly correlate with the number of occurrences in the training set, capturing "common" memorization such as familiar phrases, public knowledge or templated texts. In this paper, we provide a principled perspective inspired by a taxonomy of human memory in Psychology. From this perspective, we formulate a notion of counterfactual memorization, which characterizes how a model's predictions change if a particular document is omitted during training. We identify and study counterfactually-memorized training examples in standard text datasets. We further estimate the influence of each training example on the validation set and on generated texts, and show that this can provide direct evidence of the source of memorization at test time.