In 2018, Streamr announced a partnership with Daisy AI, Japan, an AI platform using blockchain for deep learning, based in Japan. Streamr intended to become Daisy AI's official data provider to exclusively sell data from Streamr's decentralized data Marketplace. Daisy AI planned to purchase data for a wide range of purposes, including forecasting stock and cryptocurrency prices, economy insights, footfall and traffic. Shohei Ohsawa, representative director of Daisy AI and associate professor at the University of Tokyo, recently made a series of racist and offensive statements on Twitter. He wrote that "Daisy does not hire Chinese people."

CLEVELAND–Scientists from the Case Western Reserve University digital imaging lab, already pioneering the use of Artificial Intelligence (AI) to predict whether chemotherapy will be successful, can now determine which lung-cancer patients will benefit from expensive immunotherapy. And, once again, they're doing it by teaching a computer to find previously unseen changes in patterns in CT scans taken when the lung cancer is first diagnosed compared to scans taken after the first 2-3 cycles of immunotherapy treatment. And, as with previous work, those changes have been discovered both inside–and outside–the tumor, a signature of the lab's recent research. "This is no flash in the pan–this research really seems to be reflecting something about the very biology of the disease, about which is the more aggressive phenotype, and that's information oncologists do not currently have," said Anant Madabhushi, whose Center for Computational Imaging and Personalized Diagnostics (CCIPD) has become a global leader in the detection, diagnosis and characterization of various cancers and other diseases by meshing medical imaging, machine learning and AI. Currently, only about 20% of all cancer patients will actually benefit from immunotherapy, a treatment that differs from chemotherapy in that it uses drugs to help your immune system fight cancer, while chemotherapy uses drugs to directly kill cancer cells, according to the National Cancer Institute.

Black Friday or Cyber Monday, take your pick; it's that time of year again. If you're in the market for a smartphone -- and it's statistically likely you are, given that 403.5 million handsets shipped last holiday season -- there's no better month to seek out promotions, discounts, and limited-time deals on new devices. Samsung is hosting a sale on Galaxy phones including the Galaxy S10e, S10, S10 Plus, and S10 5G, and OnePlus recently knocked $150 off the price of the OnePlus 7 Pro. Carriers like T-Mobile, Sprint, AT&T, and Verizon are awarding up to $700 in trade-in credits, and as for retailers, there's the usual doorbusters. It's almost too much of a good thing -- particularly if you aren't committed to a brand, a model, or a manufacturer. Conventional wisdom would have you judge a device by its screen or perhaps its camera, but we took a different tack last year with our guide to the best phones for the AI enthusiast.

Data governance recently became a vitally important personal and political topic. Security and privacy are at the heart of new regulatory efforts like GDPR in the EU and CCPA in California. When asked, individuals rank data protections among their top privacy priorities. Companies know the importance of proper data governance, as well. Yet, research suggests their confidence in their own know-how may be overinflated.

When it comes to things like showing us the right search results at the right time, A.I. can often seem like it's darn close to being able to read people's minds. But engineers at Russian robotics research company Neurobotics Lab have shown that artificial intelligence really can be trained to read minds -- and guess what videos users are watching based entirely on their brain waves alone. "We have demonstrated that observing visual scenes of different content affects the human brain waves, so that we can distinguish the scene categories from [one another] by analyzing the corresponding EEG (electroencephalogram) signal," Anatoly Bobe, an engineer of Neurorobotics Lab in Moscow, told Digital Trends. "We [then] created a system for reconstructing the images from EEG signal features." The researchers trained the A.I. by showing it video clips of different objects, alongside the brain wave recordings of the people watching them. This allowed the deep learning neural network to learn the features commonly seen in brain wave activity when people were viewing particular types of video content.

The automatic identification of complex features in images has already become a reality thanks to artificial neural networks. Some examples of software exploiting this technique are Facebook's automatic tagging system, Google's image search engine and the animal and plant recognition system used by iNaturalist. We know that these networks are inspired by the human brain, but their working mechanism is still mysterious. New research, conducted by SISSA in association with the Technical University of Munich and published for the 33rd Annual NeurIPS Conference, proposes a new approach for studying deep neural networks and sheds new light on the image elaboration processes that these networks are able to carry out. Similar to what happens in the visual system, neural networks used for automatic image recognition analyse the content progressively, through a chain of processing stages.

Today we can see how computer vision (CV) systems are revolutionizing whole industries and business functions with successful applications in healthcare, security, transportation, retail, banking, agriculture, and more. In 2019, we saw lots of novel architectures and approaches that further improved the perceptive and generative capacities of visual systems. To help you navigate through the overwhelming number of great computer vision papers presented this year, we've curated and summarized the top 10 CV research papers of 2019 that will help you understand the latest trends in this research area. The papers that we selected cover optimization of convolutional networks, unsupervised learning in computer vision, image generation and evaluation of machine-generated images, visual-language navigation, captioning changes between two images with natural language, and more. Subscribe to our AI Research mailing list at the bottom of this article to be alerted when we release new summaries. If you'd like to skip around, here are the papers we featured: Convolutional Neural Networks (ConvNets) are commonly developed at a fixed resource budget, and then scaled up for better accuracy if more resources are available.

How are data and AI transforming transportation? While our growing dependencies on mobile phones stand to threaten road safety and increase rates of distracted driving, other technology innovations can work in safety's favor. Developments in 5G networks, autonomous vehicles, and artificial intelligence are poised to transform the way we drive and the safety of our roads. Today, road crews have to physically go on-site to inspect a problem and determine what next steps are required. But through new video and sensor data, road maintenance crews will receive alerts of life-threatening hazards faster than ever.

Besides "fake news", there's been another term which has caused many debates around it: deepfake. These seemingly realistic videos that are, in fact, manipulated have become more problematic lately, casting a shadow on the trust people have in media. Can blockchain and artificial intelligence (AI) be used to combat deepfake, to restore the public confidence back into the system? What is "deepfake", a term which combines'deep learning' and'fake'? According to Wikipedia, it's a technique for human image synthesis based on AI.

Trillium is looking for an outstanding team member to lead and manage NASA FDL operations in the US. Your role will be to manage a multi-stakeholder program, work with experts in Artificial Intelligence and the space sciences and produce world-class results for the benefit of humankind. The right person for this role will be proactive and comfortable working with multiple stakeholders - including NASA, Google Cloud, Intel, Nvidia, IBM, Lockheed Martin and other leaders in AI as well as our deployment partners that use the results of our projects. This role includes working closely with our delivery partner, the SETI Institute, based in Mountain View, California and NASA Ames. If you're a born problem solver, adaptable, and great with people, please send us an application.