Paper: it’s not a very efficient way to store data, it’s not good for the environment, and it’s not cheap. It can rip, decay, get misplaced, and just a few rogue coffee drops can obscure its contents forever. It’s also expensive. According to Aragon Research, paper-based processes remain one of the largest cost centers in the enterprise, eating into 1-2% of overall revenues.
43% of enterprises say their AI and Machine Learning (ML) initiatives matter “more than we thought,” with one in four saying AI and ML should have been their top priority sooner.
50% of enterprises plan to spend more on AI and ML this year, with 20% saying they will be significantly increasing their budgets.
In the last decades, artificial intelligence has shown to be very good at achieving exceptional goals in several fields. Chess is one of them: in 1996, for the first time, the computer Deep Blue beat a human player, chess champion Garry Kasparov. A new piece of research shows now that the brain strategy for storing memories may lead to imperfect memories, but in turn, allows it to store more memories, and with less hassle than AI.
Slowing down climate change is an urgent matter. If we fail, our world will face a more extensive crisis than we experienced because of the global COVID-19 pandemic. When artificial intelligence (AI) technology helps solve a problem, problem-solving can be done quicker, and the solution is often one that would have taken longer for humans to discover. Could artificial intelligence power climate change strategy? Yes, and it’s already doing so.
With the ongoing pandemic-induced confinement orders in place and the recent launch of new gaming consoles from both Sony and Microsoft, this holiday season is expected to be the biggest yet for the video game industry, and the momentum should carry well into 2021. Add in a host of cloud-based gaming services from giants, including Google, Amazon and Microsoft, and we’re set up for one of the most eventful calendar years gaming has ever seen.
Machine Learning – The majority of software development teams believe they don’t test well. They understand that the effect of quality defects is substantial, and they invest heavily in quality assurance, but they still aren’t getting the results they want. This is not due to a lack of talent or effort — the technology supporting software testing is simply not effective. The industry has been underserved.
Recent Comments