Since the dawn of Sci-Fi cinema, society has been fascinated with Artificial Intelligence. Whenever we hear the term "AI", our first thought is typically one of a futuristic robot from movies such as Terminator, The Matrix and I, Robot. Although we might still be a few years away from robots that can think for themselves, there have been significant developments in the fields of machine learning and natural language understanding over the past few years. Applications such as Personal Assistants (Siri/Alexa), chatbots and Question-Answering bots are truly revolutionizing the way we interface with machines and go about our daily lives. Natural Language Understanding (NLU) and Natural Language Generation (NLG) are among the fastest growing applications of AI due to the increasing need to understand and derive meaning from language, with its numerous ambiguities and varied structure. According to Gartner, "By 2019, natural-language generation will be a standard feature of 90 percent of modern BI and Analytics platforms".

After a four-year run, the original Google Home has finally shuffled out of Google's smart speaker lineup, and now comes word that a successor is likely on the way. What are we likely to see in a revamped Google Home, or whatever Google ends up calling it? We already have some ideas. The Google Home smart speaker, which made its debut back in 2016, has been listed as "not available" on the Google store for weeks, and a Google rep recently confirmed to TechHive that the original Google smart speaker has indeed been discontinued. If you still want to snag a Google Home, you can grab one on Best Buy for $30Remove non-product link, a steep discount considering its original $129 price tag.

You can now buy one of those unnerving animal-like robots you might have seen on YouTube -- so long as you don't plan to use it to harm or intimidate anyone. Boston Dynamics on Tuesday started selling its four-legged Spot robots online for just under $75,000 each. The agile robots can walk, climb stairs and observe their surroundings with cameras and other sensors. But people who buy them online must agree not to arm them or intentionally use them as weapons, among other conditions. "The key goal for us is to make sure people trust robots," Michael Perry, the company's vice president for business development, said in an interview.

Apprentice.io, a startup developing a conversational AI and augmented reality platform for pharmaceutical, biotech, and chemical companies, today announced it has raised $7.5 million. CEO and cofounder Angelo Stracquatanio says the capital will enable Apprentice to scale to accommodate customer growth attributable to the pandemic. A shortage of lab workers is hastening the adoption of automation-driven "augmentation" technologies. An American Society for Clinical Pathology study revealed that the increasing workload is compelling lab managers to hire recent graduates or candidates with bachelor's degrees but no laboratory training. Automation and digital guidance tools like Apprentice's can upskill young professionals while ensuring quality standards aren't compromised.

AI and machine learning framework developer Streamlit today announced it raised $21 million in a financing round co-led by Gradient Ventures (Google's AI-focused investment arm) and GGV Capital. The company anticipates spending the bulk of the proceeds on expansion and product development, and on laying the runway for the launch of its enterprise-oriented Streamlit for Teams offering. Launching machine learning projects into production often requires stitching together internal tools. These tools can be difficult to deploy, require reasoning about client-server architecture, and don't integrate well with existing constructs and platforms. Moreover, they require frequent maintenance on the backend, which dedicated teams are sometimes too overwhelmed to provide.

Researchers and physicians at Oregon Health & Science University, using artificial intelligence and automated monitoring, have designed a method to help people with type 1 diabetes better manage their glucose levels. The research was published in the journal Nature Metabolism. "Our system design is unique," said lead author Nichole Tyler, an M.D.-Ph.D. student in the OHSU School of Medicine. "We designed the AI algorithm entirely using a mathematical simulator, and yet when the algorithm was validated on real-world data from people with type 1 diabetes at OHSU, it generated recommendations that were highly similar to recommendations from endocrinologists." That's significant because the people with diabetes typically go three to six months between appointments with their endocrinologist.

"Okay, Houston, we've had a problem here."1 The now famous words were transmitted through space down to Houston after the Apollo 13 crew attempted to stir the cryogenic oxygen tanks. The otherwise-standard procedure caused a series of electrical shorts and subsequent problems, leaving the command module unable to generate power, provide oxygen, or produce water. Bounded by space thousands of miles from Earth, the situation was dire for the Apollo 13 crew. NASA engineers on the ground had very little information. The crew shared their observations, and the spacecraft was transmitting some data, but these inputs didn't give engineers on the ground a perfect picture. To better understand what had happened and what consequences the crew faced, the NASA team in Houston used a mirrored system of the Apollo 13 spacecraft, allowing them to duplicate the situation as accurately as possible.

Have you heard about AI learning to play computer games on their own and giving tough competitions to expert Human gamers? A very popular example being Deepmind whose AlphaGo program defeated the South Korean Go world champion in 2016. Other than this there are other AI agents developed with the intent of playing Atari games like Breakout, Pong, and Space Invaders. These AI agents use Reinforcement Learning algorithms which is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning differs from supervised learning in not needing labeled input/output pairs to be presented, and in not needing sub-optimal actions to be explicitly corrected.

Recent surveys, studies, forecasts and other quantitative assessments of AI highlight the mix results of implementing AI in the enterprise; the increased adoption of Robotic Process Automation (RPA) and Work From Home (WFH) practices as a result of Covid-19; and China as the largest source of top-tier AI researchers, with more than half of them working in the US. Takeda plans to train thousands of staff to build and use software bots for themselves; based on a successful pilot with 22 employees, it estimates that the effort could automate 4.6 million hours of office work per year--the equivalent of roughly 2,000 full-time workers (but Takeda doesn't see the technology displacing anyone); RPA provider UiPath added 836 new customers in the first quarter, doubling its customer base year-over-year [Wired] U.S. employers since January have posted a total of 42,682 job ads for positions with an AI skills component, an increase of 14% from the same period last year, according to CompTIA. IDC's low-end estimate is projecting the number of global AI-related jobs this year at 927,000, up 11% from 2019. IDC's more optimistic outlook is for 969,000, a 16% gain over last year [WSJ] Akamai observed momentous internet traffic growth in March, which can be explained by new guidelines around social distancing and remote working during Covid-19. One of the observed changes is the increase in consumption of internet services over enterprise-connected devices, with a 40% increase during the month.

For all the complaints in Trump's America about immigrants and threats to suspend visas like H1B, some areas would be woefully understaffed without talent from outside the country. They include the growing sector of artificial intelligence/machine learning. A recent study from MacroPolo, a thinktank run by the Paulson Institute in Chicago (founded by former Goldman Sachs CEO Henry M Paulson) found that only 20% of the world's top A.I. researchers come from the USA, while 29% come from China. The remainder are from Europe (18%), India (8%) and elsewhere. Even though the U.S. ony accounts 20% of top tier A.I. researchers (as defined by the nationality of individuals submitting papers to the 2019 Neural Information Processing Systems (NeurIPS) conference), the MacroPolo study found that it employs 59% of the top A.I researchers in the world.