The U.S. must offer a "measured response" to Iran's downing of an American drone over the Strait of Hormuz, according to a Republican member of the House Homeland Security Committee. Failure to respond to Iranian aggression would incentivize future attacks, U.S. Rep. Mark Green, R-Tenn., told Shannon Bream on Thursday on "Fox News @Night." "I think we clearly need a measured response here," Green said. "I think the world needs to see, honestly, smoke and fire. I think Kim Jong Un needs to see smoke and fire. There's been an attack on the U.S. military and if we don't respond, we are incentivizing future attacks."

Machine learning is the holy grail of data analysis, but unfortunately, that holy grail oftentimes requires a PhD in Computer Science just to get started. Despite the incredible attention that machine learning and artificial intelligence get from the press, the reality is that there is a massive gap between the needs of companies to solve business challenges and the availability of talent for building incisive models. YC-backed Intersect Labs is looking to solve that gap by making machine learning much more widely accessible to the business analyst community. Through its platform, which is being launched fully publicly, business analysts can upload their data, and Intersect will automatically identify the right machine learning models to apply to the dataset and optimize the parameters of those models. The company was founded by Ankit Gordhandas and Aaron Fried in August of last year.

Around 8,000 technologists, half of whom are software engineers, work at RELX. Annually, the company spends $1.4bn on technology. The combination of our rich data assets, technology infrastructure and knowledge of how to use next generation technologies, such as machine learning and natural language processing, allows us to create effective solutions for our customers. Helping research chemists with Elsevier's Reaxys Reaxys enables the shortest path to chemistry research answers, supporting the early stages of drug development in the pharmaceutical industry, exploratory chemistry research in academia, and product development in industries such as chemicals and oil & gas. The amount of chemical information published each year is increasing exponentially, making it more and more challenging for research chemists to quickly find targeted and actionable information to help support their research.

In the race among tech companies to bring their voice recognition technology into the realm of personal medicine, Google is the furthest along, according to a study published on Thursday in the journal Nature Digital Medicine. Researchers Yan Fossat and Adam Palanica from lab company Klick Health in Toronto tested technology from Google, Amazon and Apple to gauge how well their services comprehended the 50 most commonly prescribed medicines and whether they could provide accurate information to users. Fossat and Palanica said they activated Google Assistant, Amazon Alexa and Apple's Siri and played individual audio clips from 46 English speaking people with the prompt, "Tell me about," followed by the medication name. "We reviewed all the literature, and identified this one area of medication comprehension that is under studied," Fossat told CNBC. "It's especially important to research these voice assistant tools, given the growing demand for them in health care."

The main story of this edition of the Tech Diaries is the Deepfakes issue that has gotten the U.S Congress freaking out. It represents the class of synthetic media generated by AI and represents another dark side of technology -- ringing alarm bells about what the implications of a sudden digital transformation can have on the society as a whole. The disruption caused by deepfakes can have serious consequences on how we differentiate right from wrong -- as if the "fake news" issue on the social media & other platforms isn't enough headache already. U.S lawmakers have started hearings on the issue, fearing the disruptive & deceptive technology may unfairly affect the upcoming U.S Presidential elections in 2020 -- as we saw, how just a simple low tech manipulation of videos of the U.S President & the House Speaker by rival groups earlier this year created headlines. The real problem starts when advanced Deep Learning algorithms are employed to create real-life images.

However, some of the biggest arguments or worries about AI have already been answered without most people noticing. Don't worry about future artificial intelligence challenges. Humans develop AI and write policy. IBM's AI platform Watson is 90% accurate in making treatment decisions in early-stage lung cancer. Self-driving cars are safer than human drivers.

In a new study published in the journal Artificial Life, a research team led by Nicholas Guttenberg and Nathaniel Virgo of the Earth-Life Science Institute (ELSI) at Tokyo Institute of Technology, Japan, and Alexandra Penn of The Centre for Evaluation of Complexity Across the Nexus (CECAN), University of Surrey UK (CRESS), examine the connection between biological evolutionary open-endedness and recent studies in machine learning, hoping that by connecting ideas from artificial life and machine learning, it will become possible to combine neural networks with the motivations and ideas of artificial life to create new forms of open-endedness. One source of open-endedness in evolving biological systems is an "arms race" for survival. For example, faster foxes may evolve to catch faster rabbits, which in turn may evolve to become even faster to get away from the faster foxes. This idea is mirrored in recent developments involving placing networks in competition with each other to produce things such as realistic images using generative adversarial networks (GANs), and to discover strategies in games such as Go, which can now easily beat top human players. In evolution, factors such as mutation can limit the extent of an arms race.

One of the many remarkable things about artificial intelligence is that while we tend to think of it as something that will have a big effect in the not-too-distant future, it is already transforming people's lives in profound and powerful ways today. In factories and warehouses, AI is improving workplace safety by scanning thousands of videos to detect potential risks. In the U.S., researchers are exploring how AI can help public health organizations around the world prevent the spread of deadly diseases like Ebola, Chikungunya, and Zika by detecting the presence of pathogens in the environment and stopping transmission to humans before outbreaks can begin. I believe this is the true promise and challenge of AI – using these new technologies to create a healthier and safer world for everyone. Now that AI has given computers the ability to recognize words and images, discover patterns in complex systems and reason and learn much like people do, it is enabling our devices to behave more naturally and more responsively.

Your ability to recognize objects is remarkable. If you see a cup under unusual lighting or from unexpected directions, there's a good chance that your brain will still compute that it is a cup. Such precise object recognition is one holy grail for artificial intelligence developers, such as those improving self-driving car navigation. While modeling primate object recognition in the visual cortex has revolutionized artificial visual recognition systems, current deep learning systems are simplified, and fail to recognize some objects that are child's play for primates such as humans. In findings published in Nature Neuroscience, McGovern Institute investigator James DiCarlo and colleagues have found evidence that feedback improves recognition of hard-to-recognize objects in the primate brain, and that adding feedback circuitry also improves the performance of artificial neural network systems used for vision applications.

A team of researchers at the Massachusetts Institute of Technology (MIT) have come up with a predictive Artificial Intelligence (AI) that can learn to see by touching and to feel by seeing. While our sense of touch gives us capabilities to feel the physical world, our eyes help us understand the full picture of these tactile signals. Robots, however, that have been programmed to see or feel can't use these signals quite as interchangeably. The new AI-based system can create realistic tactile signals from visual inputs, and predict which object and what part is being touched directly from those tactile inputs. In the future, this could help with a more harmonious relationship between vision and robotics, especially for object recognition, grasping, better scene understanding and helping with seamless human-robot integration in an assistive or manufacturing setting.