If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
MIT researchers have used AI to predict which technologies are rapidly improving -- and which ones are overhyped. In a new study, the team quantitatively assessed the future potential of 97% of the US patent system. The fastest-improving domains were predominantly software-related. They then converted their findings into an online system in which users can enter keywords to find improvement forecasts for specific technologies. Their research could give entrepreneurs, researchers, investors, and policy-makers clues about the future opportunities in tech.
AFTER A LONG and lonely lockdown, Theresa Causa was ready for love. To find it, the 40-year-old nurse practitioner in San Antonio turned to the new dating app "S'More," which helps users pair up by literally shifting the focus from physical appearances to mutual goals and interests. When matches first connect, they see only blurred versions of each other's profile photos, along with bios, hobbies and answers to prompts like "What are your top 3 qualities in a match?" As they exchange messages, their photos gradually un-blur. "I was, like, 'This is for me,'" said Ms. Causa.
Global Machine Learning Market Report 2021 is latest research study released by HTF MI evaluating the market risk side analysis, highlighting opportunities and leveraged with strategic and tactical decision-making support. The report provides information on market trends and development, growth drivers, technologies, and the changing investment structure of the Global Machine Learning Market. Some of the key players profiled in the study are Microsoft Corporation, IBM Corporation, SAP SE, SAS Institute, Google, Amazon Web Services, Baidu, BigML, Fair Isaac Corporation (FICO), Hewlett Packard Enterprise Development LP (HPE), Intel Corporation, KNIME.com AG, RapidMiner, Angoss Software Corporation, H2O.ai, Alpine Data, Domino Data Lab, Dataiku, Luminoso Technologies, TrademarkVision, Fractal Analytics, TIBCO Software, Teradata, Dell, Oracle Corporation. The study provides comprehensive outlook vital to keep market knowledge up to date segmented by SMEs & Large Enterprises,, Cloud Deployment & On-premise Deployment and 18 countries across the globe along with insights on emerging & major players.
These, in descending order, are the top 10 most-invested-in emerging technologies in the United States, as ranked by number of deals. If you want to get a sense of which technologies will be shaping our future in the years to come, this probably isn't a bad starting point. The figures come from a massive new artificial intelligence forecasting engine built by the French intelligence firm, L'Atelier. "We make sense of tomorrow, today," claims the website of the small company, which has been doing its smart technological guesswork (with humans instead of A.I.) since 1978. "I call it the technology intelligence engine," said Gio Tarraf, the bearded, yet boyish, 33-year-old who built the new model.
Almost every organization out there has databases brimming with data dedicated to some project or application. And for some reason, some companies still shy away from it, sticking with the good old spreadsheets. While there's nothing wrong with it fundamentally, oftentimes, some of this siloed information is restricted or simply not accessible to other parts of the organization, for whatever reason. And there's information that's hidden away from your customers -- information that they will need access to either for troubleshooting or for simply starting up. A test conducted by BBC in the UK back in 2009 showed that customers had to wait up to 24 minutes to access customer service of some kind.
Although topics like qubit scalability, error correction and the race to quantum supremacy highlight the current state of quantum computing, recently there has been a lot of discussion around use cases and applications of the available NISQ systems. The technology has reached a point of maturity where early adopters are looking into the possibility of squeezing out some quantum advantage, now or in the very near future. One direction that has been getting a lot of attention is quantum-machine learning or QML. This is not very surprising considering the massive strides made in machine learning over just the past few years. From breakthroughs in predicting protein folding, to deep fakes and the famous GTP3, these systems are sophisticated, impressively versatile and expected to, or already have, revolutionize many fields and industries.
The clues were there at WWDC 2021 as to Apple's next big thing. Apple has been laying the groundwork for it for years, and I'd say that by this time next year, it'll be here. Must read: I just found my lost AirTag… you'll never guess where it went And a lot of the pieces are already in place for this. For example, take Live Text in photos, with its associated visual look-up feature. Having the ability to pull text from real-world scenes and pull up data on what the user is looking at would be a cornerstone feature of any AR glasses.
While there have been strides taken in filling up the gender gap across fields, especially engineering and technology-based, there are still miles to go. At times, though, it is due to part societal misconceptions and part lack of knowledge about different fields that we have a gap to fill. That said, what we need is information on all available career and educational prospects that help with choosing the path forward. One such option is machine learning. Machine learning (ML), for the uninitiated like me, is the science of getting computers ie the machines to study and behave like humans, and improve their learning over time automatically, from the fed information and data that comes in the form of observations and real-world interactions.
Lee Sedol, a world-class Go Champion, was flummoxed by the 37th move Deepmind's AlphaGo made in the second match of the famous 2016 series. So flummoxed that it took him nearly 15 minutes to formulate a response. The move was strange to other experienced Go players as well, with one commentator suggesting it was a mistake. In fact, it was a canonical example of an artificial intelligence algorithm learning something that seemed to go beyond just pattern recognition in data -- learning something strategic and even creative. Indeed, beyond just feeding the algorithm past examples of Go champions playing games, Deepmind developers trained AlphaGo by having it play many millions of matches against itself.