It's hard to imagine anything less personable than a vacuum cleaner--until you give it a mind of its own. Almost as soon as iRobot released the Roomba into the world, a community of autonomous vacuum enthusiasts started giving their Roombas names, backstories, and custom wardrobes. One of the company's early TV ads acknowledged this unlikely bond, featuring people talking about their Roomba like it was a person. It's a big emotional investment in a tool whose sole purpose is to suck up filth, but Paolo Pirjanian, former CTO of iRobot, totally gets it. "There's something innate in our mind that triggers when you see something move on its own," says Pirjanian.

Resistant AI, a Prague-based security company that protects AI systems from cyberattacks and fraud, has raised $2.75 million in a round co-led by Index Ventures and Credo Ventures. Seedcamp and angel investors such as Daniel Dines and Michal Pechoucek also participated. Deployed on top of existing systems, Resistant AI provides a typical security layer for financial services, such as protecting payments and detecting fraud or money laundering -- and also detects forged documents or spots sophisticated attackers attempting to copy the underlying ML model. "Historically, all systems that make high-value financial decisions become targeted. This is already happening with the automated systems deployed by our fintech and financial customers and we are here to protect them," says Martin Rehak, the startup's co-founder and CEO.

Even now, all you have to do is Google search'AI 2017' to find headlines like these: '2017 laid the foundation for faster, smarter AI in 2018' 'All the creepy, crazy and amazing things that happened in AI in 2017' AI took the tech industry by storm. Swarm AI correctly predicted TIME's Person of the Year to be Donald Trump, AI moved into the household through the Amazon Echo and Google Home, and Google's DeepMind AlphaGo Zero conquered the 2,000-year-old board game'Go' through machine learning. If you didn't already know: AlphaGo literally recreated itself without the help of humans, using reinforcement learning to surpass the abilities of world champion Le Sedol and become the best Go player in the world. In 2018, poker bot Libratus was the first to beat 15 top human players, and American technology company Nvidia created AI that could mimic your facial features, handwriting, and voice. They created'celebrities' that don't even exist. Though it didn't impress everyone, with comments like: The iceberg that would later reveal the all-conquering and all-powerful force reckoned to control our entire lives – otherwise known as artificial intelligence.

Quite recently, the US Patent and Trademark Office (USPTO) has ruled that Artificial Intelligence (AI) systems can't get the credit of a legal inventor in a patent filing. The ruling has come as a response to two Patent Applications filed corresponding to a flashing light and food container, which were created by an AI-based system known as DABUS. The USPTO has presented a lot of arguments concerning the same. The first and foremost argument corresponds to the Patent Law in the US, which repeatedly refers to the patent inventors or innovators by using humanlike pronouns like'himself' and'herself' and terms like'whoever.' The team that filed the patent applications had argued by saying that the patent law's references to an inventor as an'individual' could very well be applied to machines too.

The report "Artificial Intelligence in Agriculture Market by Technology (Machine Learning, Computer Vision, and Predictive Analytics), Offering (Software, Hardware, AI-as-a-Service, and Services), Application, and Geography - Global Forecast to 2026", is estimated to be USD 1.0 billion in 2020 and is projected to reach USD 4.0 billion by 2026, at a CAGR of 25.5% between 2020 and 2026. The market growth is driven by the increasing implementation of data generation through sensors and aerial images for crops, increasing crop productivity through deep-learning technology, and government support for the adoption of modern agricultural techniques. Browse 81 market data Tables and 40 Figures spread through 152 Pages and in-depth TOC on "Artificial Intelligence in Agriculture Market by Technology (Machine Learning, Computer Vision, and Predictive Analytics), Offering (Software, Hardware, AI-as-a-Service, and Services), Application, and Geography - Global Forecast to 2026" The market for drone analytics is expected to grow at the highest rate due to its extensive use for diagnosing and mapping to evaluate crop health and to make real-time decisions. Favorable government mandates for the use of drones in agriculture are also expected to fuel the growth of the drone analytics market. Increasing awareness among farm owners regarding the advantages associated with AI technology is expected to further fuel the growth of the AI in agriculture market.

Recently, while waiting for a video meeting to begin, I noticed a young child in the background in the office of one of the participants. As children are usually more interesting than the average videoconference, I was curious about what she was doing. I was amused when I saw her looking at a magazine cover photo and trying to enlarge it with her thumb and forefinger. Her look was one of perplexity, as she realized the image couldn't be manipulated using the pinch-to-zoom motion, such as when using a smartphone. After a few tries, she gave up and walked away, seemingly unconcerned and no longer interested in the photo.

In a pair of papers accepted to the International Conference on Learning Representations (ICLR) 2020, MIT researchers investigated new ways to motivate software agents to explore their environment and pruning algorithms to make AI apps run faster. Taken together, the twin approaches could foster the development of autonomous industrial, commercial, and home machines that require less computation but are simultaneously more capable than products currently in the wild. One team created a meta-learning algorithm that generated 52,000 exploration algorithms, or algorithms that drive agents to widely explore their surroundings. Two they identified were entirely new and resulted in exploration that improved learning in a range of simulated tasks -- from landing a moon rover and raising a robotic arm to moving an ant-like robot. The team's meta-learning system began by choosing a set of high-level operations (e.g., basic programs, machine learning models, etc.) to guide an agent to perform various tasks, like remembering previous inputs, comparing and contrasting current and past inputs, and using learning methods to change its own modules.

The AI was adapted from an existing system trained to identify people most at risk from the flu, using millions of records from Maccabi going back 27 years. To make its predictions, the system draws on a range of medical data, including a person's age, BMI, health conditions such as heart disease or diabetes, and previous history of hospital admissions. The AI can trawl through a vast number of records and spot at-risk individuals who might have been missed otherwise. Maccabi also uses the AI to help determine the level of treatment the people it flags might require if they fall sick--whether they should be cared for at home, put up in a quarantine hotel, or admitted to hospital. The organization says it is now talking to major US health providers that are interested in using the AI to fast-track their own high-risk patients.

Nothing is more comforting than being greeted by your favorite drink just as you walk through the door of the corner café. While a thoughtful barista knows you take a macchiato every Wednesday morning at 8:15, it's much more difficult in a digital space for your preferred brands to personalize your experience. Talking Data, China's largest third-party mobile data platform, understands that everyday choices and behaviors paint a picture of who we are and what we value. Currently, Talking Data is seeking to leverage behavioral data from more than 70% of the 500 million mobile devices active daily in China to help its clients better understand and interact with their audiences. So, the business problem is to predict the demographic characteristics of the users using their app usage,geographical location and device properties.

Robots, it seems, are lucky that way. Most every organization has been thrust into the future of work faster than prognosticators dared imagine. What will determine failure or success in this brave new world of work? The global pandemic has sidelined workers across an unthinkable swath of sectors during a particularly tight labor market. Automation solutions that were unthinkable a twenty years ago have blossomed thanks to the convergence of technologies like machine vision, machine learning & AI, open-source robotic operating systems, and mobile components and sensors.