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No industry will be spared.

The pharmaceutical business is perhaps the only industry on the planet, where to get the product from idea to market the company needs to spend about a decade, several billion dollars, and there is about 90% chance of failure. It is very different from the IT business, where only the paranoid survive but a business where executives need to plan decades ahead and execute. So when the revolution in artificial intelligence fueled by credible advances in deep learning hit in 2013–2014, the pharmaceutical industry executives got interested but did not immediately jump on the bandwagon. Many pharmaceutical companies started investing heavily in internal data science R&D but without a coordinated strategy it looked more like re-branding exercise with the many heads of data science, digital, and AI in one organization and often in one department. And while some of the pharmaceutical companies invested in AI startups no sizable acquisitions were made to date. Most discussions with AI startups started with “show me a clinical asset in Phase III where you identified a target and generated a molecule using AI?” or “how are you different from a myriad of other AI startups?” often coming from the newly-minted heads of data science strategy who, in theory, need to know the market.

However, some of the pharmaceutical companies managed to demonstrate very impressive results in the individual segments of drug discovery and development. For example, around 2018 AstraZeneca started publishing in generative chemistry and by 2019 published several impressive papers that were noticed by the community. Several other pharmaceutical companies demonstrated impressive internal modules and Eli Lilly built an impressive AI-powered robotics lab in cooperation with a startup.

However, it was not possible to get a comprehensive overview and comparison of the major pharmaceutical companies that claimed to be doing AI research and utilizing big data in preclinical and clinical development until now. On June 15th, one article titled “The upside of being a digital pharma player” got accepted and quietly went online in a reputable peer-reviewed industry journal Drug Discovery Today. I got notified about the article by Google Scholar because it referenced several of our papers. I was about to discard the article as just another industry perspective but then I looked at the author list and saw a group of heavy-hitting academics, industry executives, and consultants: Alexander Schuhmacher from Reutlingen University, Alexander Gatto from Sony, Markus Hinder from Novartis, Michael Kuss from PricewaterhouseCoopers, and Oliver Gassmann from University of St. Gallen.

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Regeneron rose as much as 2.4% in the stock market today, touching a record high of 655.93. Shares seesawed in a small range Thursday before ending the day up a small fraction, at 640.63. The peak price topped Regeneron stock’s previous high of 653.53, touched on Tuesday. Regeneron is part of the IBD Biomed/Biotech group, which ranks No. 14 out of 197 industry groups tracked. The group is just off its all-time high, which it hit on June 23.

Regeneron Pharmaceuticals (REGN) jumped to an all-time high Thursday, its second of the week, as analysts remain bullish on Regeneron stock due to its coronavirus treatment and other businesses.

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Panasonic has begun testing robotic mobility devices at the newly constructed Takanawa Gateway train station in Tokyo.

The effort is art of a plan to bring a series of automated services to the airport and surrounding facilities that are part of a massive redevelopment project in the surrounding Shinagawa business district.

Three mobility devices, essentially intelligent electric wheelchairs, will be used as a single group in the trial. The experiment will focus on ensuring the safety of passengers with mobility issues as they are transported throughout the huge facilities.

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Google today announced that it has signed up Verizon as the newest customer of its Google Cloud Contact Center AI service, which aims to bring natural language recognition to the often inscrutable phone menus that many companies still use today (disclaimer: TechCrunch is part of the Verizon Media Group). For Google, that’s a major win, but it’s also a chance for the Google Cloud team to highlight some of the work it has done in this area. It’s also worth noting that the Contact Center AI product is a good example of Google Cloud’s strategy of packaging up many of its disparate technologies into products that solve specific problems.

“A big part of our approach is that machine learning has enormous power but it’s hard for people,” Google Cloud CEO Thomas Kurian told me in an interview ahead of today’s announcement. “Instead of telling people, ‘well, here’s our natural language processing tools, here is speech recognition, here is text-to-speech and speech-to-text — and why don’t you just write a big neural network of your own to process all that?’ Very few companies can do that well. We thought that we can take the collection of these things and bring that as a solution to people to solve a business problem. And it’s much easier for them when we do that and […] that it’s a big part of our strategy to take our expertise in machine intelligence and artificial intelligence and build domain-specific solutions for a number of customers.”

The company first announced Contact Center AI at its Cloud Next conference two years ago and it became generally available last November. The promise here is that it will allow businesses to build smarter contact center solutions that rely on speech recognition to provide customers with personalized support while it also allows human agents to focus on more complex issues. A lot of this is driven by Google Cloud’s Dialogflow tool for building conversational experiences across multiple channels.

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The coming two decades are scheduled to be very interesting decades for human space exploration and colonization. One of the things on America’s agenda for space exploration is creating humanity’s first Mars base, which will most likely happen in the 2030s and 2040s. If you are interested in what this Moon base will look like, please take a look at this video!

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SoftBank CEO Masayoshi Son’s Vision Fund has been impossible to ignore since its inception, pumping billions upon billions of dollars into tech companies like WeWork and Uber. Now, a string of high-profile losses and the coronavirus pandemic have put the fund deeply in the red. Bloomberg journalists Pavel Alpeyev, Sarah McBride and Tim Culpan break down the controversial investment strategies that have led to this critical moment for Son’s unprecedented fund.

Video by vicky feng and alan jeffries

#SoftBank #Epics #Business

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In fact, in a recent paper in Royal Society Open Science, researchers showed that AI tasked with maximizing returns is actually disproportionately likely to pick an unethical strategy in fairly general conditions. Fortunately, they also showed it’s possible to predict the circumstances in which this is likely to happen, which could guide efforts to modify AI to avoid it.

The fact that AI is likely to pick unethical strategies seems intuitive. There are plenty of unethical business practices that can reap huge rewards if you get away with them, not least because few of your competitors dare use them. There’s a reason companies often bend or even break the rules despite the reputational and regulatory backlash they could face.

Those potential repercussions should be of considerable concern to companies deploying AI solutions, though. While efforts to build ethical principles into AI are already underway, they are nascent and in many contexts there are a vast number of potential strategies to choose from. Often these systems make decisions with little or no human input and it can be hard to predict the circumstances under which they are likely to choose an unethical approach.

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Researchers from the UK and Switzerland have found a mathematical means of helping regulators and business police Artificial Intelligence systems’ biases towards making unethical, and potentially very costly and damaging choices.

The collaborators from the University of Warwick, Imperial College London, and EPFL – Lausanne, along with the strategy firm Sciteb Ltd, believe that in an environment in which decisions are increasingly made without human intervention, there is a very strong incentive to know under what circumstances AI systems might adopt an unethical strategy—and to find and reduce that risk, or eliminate entirely, if possible.

Artificial intelligence (AI) is increasingly deployed in commercial situations. Consider for example using AI to set prices of insurance products to be sold to a particular customer. There are legitimate reasons for setting different prices for different people, but it may also be more profitable to make certain decisions that end up hurting the company.

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It is often said that the 21st century will belong to China because China will grow its military, develop its economy, and completely integrate Hong Kong. However, in this video, I argue that the 21st Century won’t belong to China because it won’t take advantage of space resources and because it will attempt to grab more than it can chew in its ambitious endeavors.

PS: The stock footage from this photo comes from Videvo!

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Artificial intelligence (AI) is transforming how enterprises analyze and process information. It is also shifting from theoretical to real-world technology. Companies are deploying AI technologies to boost efficiency, reduce costs, and grow sales and profitability. The technology can also reduce marketing waste by predicting what works. It is the most impactful innovation of our lifetime, and it will create new winners and losers across entire industries.

According to Gartner, artificial intelligence will create $2.9 trillion in business value and 6.2 billion hours of worker productivity globally in 2021. Most of that value will be realized by enterprises that implement AI in functions such as sales management, customer service, manufacturing, and logistics. With improvements in natural language processing, employees and users can easily communicate with machine-learning interfaces.

Let’s look at how AI applications are revamping digital enterprises and the retail industry.

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