Alphabet X-birthed Intrinsic made its big debut last September. The subsidiary looks to buck its parent company's somewhat spotty robotics record with a software-first approach. Specifically, the company is looking to make manufacturing robots more intelligent -- a concept that no doubt excites many in the space amid pandemic-fueled demand for automation. Today it made its second big piece of news: an acquisition. Intrinsic is acquiring fellow AI/robotic intelligence firm Vicarious.

The end of June saw San Francisco-based brand strategy and design studio Landscape unveil a brand identity and site for Vicarious, an intelligent robotics start-up backed by $150m in funding from Elon Musk, Samsung and ABB. Thanks to AI and machine learning, robots are becoming more versatile and affordable than ever, and after 10 years of advanced AI research, Vicarious is now commercialising its offering as the world's first provider of Robots-as-a-Service. Landscape worked with the company to overhaul its brand strategy as it evolved from research leader to automation expert. Because automation is not the same for small businesses as it is for large enterprises, Vicarious needed an evolved brand that could signal different benefits to different audiences. To articulate this effectively, Landscape updated the positioning and messaging site wide, in tandem with a new Saul Bass-influenced design direction, and a new site architecture. "As part of this evolution, the brand had to signal serious business value to companies of all sizes, from small companies to global enterprises," explain Mackenzie Brookshire, associate creative director at Landscape and its strategy director Frank H Vial. "The visual system reflects the role of manufacturing in relation to human progress -- a balance of future technology and human potential, looking forward and inward. The brand feels purposeful, intentional, and powerful while remaining connected to craft and warmth."

Here are a few predictions about how several industries that impact our everyday lives will be impacted by AI not only this year but beyond. The buzz surrounding AI and its impact in 2020 and beyond shows no signs of slowing down. Driven by the emergence of virtual assistants, such as the Alexa, Siri, and Google Assistant ecosystems of devices, AI has now been incorporated into the everyday life of consumers. While it's impossible to predict the future with certainty, technologies that incorporate AI and automation are maturing at an incredibly rapid rate across some industries. Here are a few predictions about how several industries that impact our everyday lives – specifically healthcare, manufacturing, and mobility – will be impacted by AI not only this year but beyond.

At the offices of startup Vicarious in Union City, where the Bay Area's sprawl abuts rolling hills, 10 robot arms tirelessly place travel-sized beauty products into bins on a conveyor belt. Each gray arm ends in a suction-cup-tipped finger that makes a high pitched whine as it plucks items such as antiperspirant or hand lotion from crowded boxes. Vicarious buys standard industrial robots, enhances them with its software, and contracts them out the way a temp agency does workers--charging per task completed or at an hourly rate. In Baltimore, Vicarious robots assemble sampler packs for makeup company Sephora, work previously done exclusively by humans. Vicarious CEO and cofounder D. Scott Phoenix says the deal demonstrates his business model: Create artificial intelligence software that makes industrial robots smart enough to perform jobs previously done only by people.

Here's a look at the top technology trends that will influence us. AI is now part of everyday life, driven by the emergence of a device ecosystem including Alexa, Siri, and Google Assistant. In 2020, emotion recognition and computer vision will scale and AI will have a breakout moment in manufacturing. U.S. startups Vicarious, Kindred, and Osaro stand out in using AI technologies for manufacturing. Kindred's technology is used to automate part of distribution for apparel brands such as GAP.

Industry Report "Cognitive Computing Market" provides a clear picture of the Current Market Scenario which includes past and estimated future size with respect to Value and Volume, Technological Advancement, Macro Economical and Governing Factors in the Cognitive Computing market. Cognitive Computing is defined as the technology based on the principle of artificial intelligence, signal processing, machine learning, and natural language processing (NLP) among others technology. It brings human like intelligence for a many business applications which will include big data. Cognitive Computing is a well-known technology basically specialized for processing and analyzing large and unstructured datasets. The major drivers of the cognitive computing market are the advancements in computing platforms like cloud, mobile, and big data analytics which will drive the growth of the market in the forecast period.

The model is a neural network utilising HTM neurons [7] which resemble biological pyramidal neurons. These neurons are more complex than conventional artificial neural network neurons, with multiple groups and types of input connections (dendrites) with different functions. There are dendrites that are stimulators, and those that are modulatory, and predict activations. There is an input and output layer (resembling two of the cortical pyramidal cell layers) with feedback and lateral input connections. Neurons are arranged into columns that cover a subset of the input space.

Vid Robots normally need to be programmed in order to get them to perform a particular task, but they can be coaxed into writing the instructions themselves with the help of machine learning, according to research published in Science. Engineers at Vicarious AI, a robotics startup based in California, USA, have built what they call a "visual cognitive computer" (VCC), a software platform connected to a camera system and a robot gripper. Given a set of visual clues, the VCC writes a short program of instructions to be followed by the robot so it knows how to move its gripper to do simple tasks. "Humans are good at inferring the concepts conveyed in a pair of images and then applying them in a completely different setting," the paper states. "The human-inferred concepts are at a sufficiently high level to be effortlessly applied in situations that look very different, a capacity so natural that it is used by IKEA and LEGO to make language-independent assembly instructions."

Five years ago, the coders at DeepMind, a London-based artificial intelligence company, watched excitedly as an AI taught itself to play a classic arcade game. They'd used the hot technique of the day, deep learning, on a seemingly whimsical task: mastering Breakout,1 the Atari game in which you bounce a ball at a wall of bricks, trying to make each one vanish. Deep learning is self-education for machines; you feed an AI huge amounts of data, and eventually it begins to discern patterns all by itself. In this case, the data was the activity on the screen--blocky pixels representing the bricks, the ball, and the player's paddle. The DeepMind AI, a so-called neural network made up of layered algorithms, wasn't programmed with any knowledge about how Breakout works, its rules, its goals, or even how to play it.

Some say artificial intelligence needs to learn like a child. Babies are born with instincts that help us learn common sense, so far elusive for AI algorithms. It's a Saturday morning in February, and Chloe, a curious 3-year-old in a striped shirt and leggings, is exploring the possibilities of a new toy. Her father, Gary Marcus, a developmental cognitive scientist at New York University (NYU) in New York City, has brought home some strips of tape designed to adhere Lego bricks to surfaces. Chloe, well-versed in Lego, is intrigued. But she has always built upward. Could she use the tape to build sideways or upside down?