Artificial intelligence may not be human, but that doesn't make it exempt from the kind of bias almost every person displays. That's because we've been building prejudice into our AI, which learns both the good and the bad from human creators. It's a problem, scientists say, with a hidden benefit: By trying to understand how machines pick up human bias, we might in turn be able to learn how we acquire those biases ourselves. In 2017, Joanna Bryson, a computer scientist and AI specialist at the University of Bath, fed around 840 billion words--from tweets, the US Declaration of Independence, Reddit threads, and many other sources--into a purely statistical machine-learning model to see whether it would form biases based on the implicit linguistic patterns it found. Next, she told the machine to create related clusters of words.

Montreal, October 22 2018 -- The Board of Trustees of the Neural Information Processing Systems Foundation has decided not to change the name of their main conference. The Board has been engaged in ongoing discussions concerning the name of the Neural Information Processing Systems, or NIPS, conference. The current acronym, NIPS, has undesired connotations. The Name-of-NIPS Action Team was formed, in order to better understand the prevailing attitudes about the name. The team conducted polls of the NIPS community requesting submissions of alternative names, rating the existing and alternative names, and soliciting additional comments.

Notice that when we defined the blood factor for the three patients, we specified an additional vector of four possible blood types using the levels parameter. As a result, even though our data included only types O, AB, and A, all the four types are stored with the blood factor as indicated by the output. Storing the additional level allows for the possibility of adding data with the other blood types in the future. It also ensures that if we were to create a table of blood types, we would know that the B type exists, despite it not being recorded in our data.

Toptal, a global network of top talent in business, design, and technology that enables companies to scale their teams, on-demand, today announced the launch of its two new on-demand talent specializations to meet the rising demand for skilled artificial intelligence and data science engineers. Tapping into Toptal's private network of highly skilled software professionals, the new specialized service will connect organizations with freelance artificial intelligence and data science professionals who are experts in machine learning, deep learning, data architecture, and data mining. "Businesses across every sector are moving quickly to leverage the power of artificial intelligence and data science optimization. For many of them, access to high-quality talent is the critical hurdle," said Taso Du Val, Toptal co-founder and CEO. "With these service offerings, Toptal is providing businesses with the unique opportunity to quickly staff their AI and data science initiatives with elite talent. Our company was made by engineers for engineers, so we care deeply about matching our clients with experts who have the exact skills and real-world experience they need to realize their goals."

The new service offerings will leverage Toptal's unparalleled network of global talent to help businesses staff artificial intelligence and data science projects Toptal, a global network of top talent in business, design, and technology that enables companies to scale their teams, on-demand, today announced the launch of its two new on-demand talent specializations to meet the rising demand for skilled artificial intelligence and data science engineers. Tapping into Toptal's private network of highly skilled software professionals, the new specialized service will connect organizations with freelance artificial intelligence and data science professionals who are experts in machine learning, deep learning, data architecture, and data mining. "Businesses across every sector are moving quickly to leverage the power of artificial intelligence and data science optimization. For many of them, access to high-quality talent is the critical hurdle," said Taso Du Val, Toptal co-founder and CEO. "With these service offerings, Toptal is providing businesses with the unique opportunity to quickly staff their AI and data science initiatives with elite talent. Our company was made by engineers for engineers, so we care deeply about matching our clients with experts who have the exact skills and real-world experience they need to realize their goals."

Music has always been a big business for Apple (NASDAQ:AAPL). In creating iTunes and the iPod in the early 2000s, Apple was doing more than making a gadget -- it became the guardian of consumers' prized music collections. The iPod wasn't the first portable MP3 player, but it provided a superior user experience with its trademark white color and circular navigation wheel. Apple integrated the iPod tightly with iTunes and its macOS software, creating a music ecosystem. Music remained important at Apple with the 2014 acquisition of Beats Electronics, a headphone company, which brought with it music talent Dr. Dre and hit-making record producer Jimmy Iovine.

Accompanying an NYT series on artificial intelligence today is a piece of art -- seen above -- that's very unlike the newspaper's usual imagery: It represents AI, and it's drawn by AI. Why it matters: Artists are using increasingly powerful machine-learning algorithms to help produce fiction, film, and visual art. Incapable of creativity on their own, they can be programmed to act as a formidable artistic tool.

Yesterday, Google announced an AI product called Duplex, which is capable of having human-sounding conversations. I am genuinely bothered and disturbed at how morally wrong it is for the Google Assistant voice to act like a human and deceive other humans on the other line of a phone call, using upspeak and other quirks of language. "Hi um, do you have anything available on uh May 3?" If Google created a way for a machine to sound so much like a human that now we can't tell what is real and what is fake, we need to have a talk about ethics and when it's right for a human to know when they are speaking to a robot. In this age of disinformation, where people don't know what's fake news… how do you know what to believe if you can't even trust your ears with now Google Assistant calling businesses and posing as a human? That means any dialogue can be spoofed by a machine and you can't tell.

Unmanned Aerial Vehicles (UAVs) have recently rapidly grown to facilitate a wide range of innovative applications that can fundamentally change the way cyber-physical systems (CPSs) are designed. CPSs are a modern generation of systems with synergic cooperation between computational and physical potentials that can interact with humans through several new mechanisms. The main advantages of using UAVs in CPS application is their exceptional features, including their mobility, dynamism, effortless deployment, adaptive altitude, agility, adjustability, and effective appraisal of real-world functions anytime and anywhere. Furthermore, from the technology perspective, UAVs are predicted to be a vital element of the development of advanced CPSs. Therefore, in this survey, we aim to pinpoint the most fundamental and important design challenges of multi-UAV systems for CPS applications. We highlight key and versatile aspects that span the coverage and tracking of targets and infrastructure objects, energy-efficient navigation, and image analysis using machine learning for fine-grained CPS applications. Key prototypes and testbeds are also investigated to show how these practical technologies can facilitate CPS applications. We present and propose state-of-the-art algorithms to address design challenges with both quantitative and qualitative methods and map these challenges with important CPS applications to draw insightful conclusions on the challenges of each application. Finally, we summarize potential new directions and ideas that could shape future research in these areas.

Recent progress in deep learning for audio synthesis opens the way to models that directly produce the waveform, shifting away from the traditional paradigm of relying on vocoders or MIDI synthesizers for speech or music generation. Despite their successes, current state-of-the-art neural audio synthesizers such as WaveNet and SampleRNN suffer from prohibitive training and inference times because they are based on autoregressive models that generate audio samples one at a time at a rate of 16kHz. In this work, we study the more computationally efficient alternative of generating the waveform frame-by-frame with large strides. We present SING, a lightweight neural audio synthesizer for the original task of generating musical notes given desired instrument, pitch and velocity. Our model is trained end-to-end to generate notes from nearly 1000 instruments with a single decoder, thanks to a new loss function that minimizes the distances between the log spectrograms of the generated and target waveforms. On the generalization task of synthesizing notes for pairs of pitch and instrument not seen during training, SING produces audio with significantly improved perceptual quality compared to a state-of-the-art autoencoder based on WaveNet as measured by a Mean Opinion Score (MOS), and is about 32 times faster for training and 2, 500 times faster for inference.