Diligent Robotics, maker of an autonomous robot assistant for hospitals, has raised $3 million in seed funding. The raise was headed by True Ventures and Ubiquity Ventures, as well as Next Coast Ventures, Capital Factory, Pathbreaker Ventures, Boom Capital, Grit Ventures and other unnamed angel investors. This adds to another $2.1 million round announced back in the beginning of 2018, as well as a $725,000 National Science Foundation grant. This week's funding news was complemented with an announcement that the startup's hospital robot, called Moxi, is exiting beta testing and has entered market with its first official rollout in a Texas Hospital. While Diligent Robotics' broad aim is to develop robotic assistants for a range of chores or activities, the company has so far focused on healthcare use cases for its technology with Moxi.

I. Is AI doing any good at all? Researchers, entrepreneurs, and policy-makers are increasingly using AI to tackle development challenges. In other words, using AI for a greater good is a real thing. However, it is becoming clear that AI poses as many threats as benefits, although the former ones are usually neglected. I do not want to get into trust, accountability, or safety issues in this short piece (if you want, here there is more), but avoiding the negative effects of AI is why incorporating a set of ethical principles into our technology development process is so paramount. Ethics plays a key role by ensuring that regulations of AI harness its potential while mitigating its risks (Taddeo and Floridi, 2018) and it would help us understand how to use responsibly the power coming from this technology.

While touching many facets of our daily lives, this advanced technology AI now walks hand in hand with us. With each passing day, we are relying more on it! Our usage of AI-based voice assistants like Siri, Alexa, and Cortana are splendid examples. As time passes more in the future, these VA's will increase, and we will become more accustomed to them! The same is happening in the eCommerce industry, where AI is reshaping it!

Novartis and Microsoft plan to use artificial intelligence to develop new drugs faster, and with greater precision. Novartis and Microsoft have agreed to a five-year partnership to use artificial intelligence (AI) to develop new drugs faster, and with greater precision. Novartis CEO Vas Narasimhan said AI's potential for personalized medicine is particularly promising, as it will help classify subgroups of patients that new drugs will most likely benefit, using clinical and preclinical datasets. Narasimhan added that these datasets were consolidated from Novartis' internal information, surmounting integration obstacles. The ultimate goal of the alliance is to use AI to fuel research, development, manufacturing, finance, sales, marketing, and acquisitions.

Launched in January this year, the Maha Agri Tech project seeks to use technology to address various cultivation risks ranging from poor rains to pest attacks, accurately predict crop-wise and area-wise yield and eventually to use this data to inform policy decisions including pricing, warehousing and crop insurance. When farmers in six districts of Maharashtra begin sowing for the coming rabi season, this project will enter its second phase where artificial intelligence and satellite imagery will be used to mitigate risks. Fields of the farmers that are part of the project will be monitored via satellite images at every stage right until the harvest. In its first phase the Maha Agri Tech project used satellite images and analysis from the Maharashtra Remote Sensing Application Centre (MRSAC) and the National Remote Sensing Centre (NRSC) in Hyderabad to assess the acreage and the conditions of select crops in select talukas. In its second phase, various data sets from diverse data providers will be combined to build yield modelling and a geospatial database of soil nutrients, rainfall, moisture stress and other parameters to facilitate location-specific advisories to farmers.

See a larger version of the cover image here. Prediction rules in deep learning are based on a forward, recursive computation through several layers. Implicit deep learning rules go much beyond, by relying on the solution of an implicit (or, "fixed-point") equation that has to be numerically solved in order to make the prediction: for a given input vector u, the predicted vector y is of the form Here, the so-called "state" n-vector x, which contains the hidden features of the model, is not expressed explicitly; rather it is implicitly defined via the "fixed-point" (or, equilibrium) equation x ϕ(Ax Bu). At first glance, the above models seem very specific. Perhaps surprisingly, they include a special case most known neural network architectures, including standard feedforward networks, CNNs, RNNs, and many more.

Mozilla is hiring a Machine Learning Intern for our Emerging Technologies team. Emerging Technologies is Mozilla's early research and development organization focused on the areas of voice assistants, speech and language, and mixed reality. Our headquarters are based in the Bay Area, but this internship opportunity is at our Berlin Office. We are engineers, designers, makers, and problem solvers. We work in the fishbowl known as the open source community, with a clear focus on making the Web better.

Using voice assistants is part of the company's Made at McDonald's campaign, meant to amplify the opportunities a career at the company can create. Google has a new way for you to move music around the room. Just say "Hey, Google" to start a song on one speaker and finish it on another. The company calls the new tool "Stream Transfer," and it's rolling out today. The idea is that you would say, "Hey, Google, play Happy by Pharrell Williams," and the song would begin playing on your Google Home speaker.

In the first decades of the 20th century, people around the world began succumbing to an entirely new cause of mortality. These new deaths, due to the dangers of the automobile, soon became accepted as a lamentable but normal part of modern life. A hundred years later, with 1.25 million people worldwide (about 30,000 in the U.S.) being killed every year in road crashes, there's now an effort to reject the perception that these deaths are normal or acceptable. As reported in the New York Times, a growing number of safety advocates, government officials, and journalists are moving away from the phrase "car accident" on the grounds that it presumes that the drivers involved are blameless--a presumption that is correct only 6 percent of the time, according to a report by the U.S. Department of Transportation. The vast majority of such incidents are caused by drivers who make mistakes, take risks, or drive while distracted or impaired.

On September 26, 2019, the US Food and Drug Administration (FDA) published six guidance documents clarifying its scope of authority and enforcement discretion policies in light of the 21st Century Cures Act (Cures Act). The long-awaited draft guidance on Clinical Decision Support (CDS) software sets forth FDA's proposed approach to regulating CDS, including software that incorporates machine learning (ML) technology. Companies developing ML software for life science applications should consider reviewing FDA's planned approach to inform their regulatory strategies. In a long-awaited move, FDA published a draft guidance on CDS software. With the rise of artificial intelligence (AI) and machine learning (ML), CDS presents a novel opportunity to analyze immensely large amounts of data for patterns or other information that may be relevant to a particular patient's diagnosis.