Artificial Intelligence (AI) has massive potential to drive disruptive innovations affecting most enterprises on the planet. However, most enterprises are struggling to succeed with AI . Simply put, AI and Data are siloed in different systems and different organizations. Enterprise data is siloed across hundreds of systems such as data warehouses, data lakes, databases and file systems that are not AI-enabled. Popular machine learning frameworks such as TensorFlow, PyTorch, and SciKit-Learn don't do data processing.

Mozilla, the nonprofit behind the free and open-source Firefox web browser, is partnering with Montreal-based artificial intelligence startup Element AI to push for ethical use of AI. To that effect, the two companies are exploring the idea of data trusts, a proposed data collection approach that aims to provide individuals with greater control over their personal information. The aim, the companies said, is to offer an alternative model to our current broken consent-based system of data collection such as the EU GDPR laws. It's easy to see why. As artificial intelligence and machine learning (ML) continues to infiltrate different aspects of our day-to-day lives, the technology is now doing more than ever -- for both good and bad.

Bio-medical engineers have created a robot so fast moving that it can catch an insect with its'tongue' - after studying nature's springiest amphibians for inspiration. Chameleons, salamanders and toads were the inspiration for a new range of soft robots which can carry out automated tasks requiring a range of movements at a fast pace. Industrial and biomedical engineers studied the stored elastic energy that the animals use to launch their sticky tongues in order to replicate the fast, non-robotic movement. Catching unsuspecting insects located up to one-and-a-half body lengths away the amphibians high-speed movements inspired researchers at the Purdue University's College, Indiana, U.S Similar to the chameleon's tongue strike, a pre-stressed pneumatic soft robot is capable of expanding five times its own length, catch a live fly beetle and retrieve it in just 120 milliseconds Catching unsuspecting insects located up to one-and-a-half body lengths away the amphibians high-speed movements helped researchers at the Purdue University's College of Engineering, Indiana, U.S. to develop a new class of entirely soft robots. These bio-inspired robots are fabricated using stretchable polymers similar to rubber bands, with internal pneumatic channels that expand upon pressurisation.

PARIS – Coastal areas currently home to 300 million people will be vulnerable by 2050 to flooding made worse by climate change, no matter how aggressively humanity curbs carbon emissions, scientists said Tuesday. By midcentury and beyond, however, choices made today will determine whether Earth's coastlines remain recognizable to future generations, they reported in the journal Nature Communications. Destructive storm surges fueled by increasingly powerful cyclones and rising seas will hit Asia hardest, according to the study. More than two-thirds of the populations at risk are in China, Bangladesh, India, Vietnam, Indonesia and Thailand. Using a form of artificial intelligence known as neural networks, the new research corrects ground elevation data that have up to now vastly underestimated the extent to which coastal zones are subject to flooding during high tide or major storms. "Sea-level projections have not changed," co-author Ben Strauss, chief scientist and CEO of Climate Central, a U.S.-based non-profit research group, told AFP. "But when we use our new elevation data, we find far more people living in vulnerable areas that we previously understood."

The authors said they had used artificial intelligence to correct systematic errors in a previous dataset that had suggested many inhabited coastal zones were at higher elevations -- and thus safer -- than they actually are. "We now understand that the threat from sea-level rise and coastal flooding is far greater than we previously thought," said Benjamin Strauss, chief executive of Climate Central and co-author of the three-year study. "It's also true that the benefits from cutting climate pollution are far greater than we previously thought – this changes the whole benefit-cost equation," Strauss told Reuters. The threat that advancing seas will overwhelm the ability of countries to build coastal defences and force many millions of people to migrate has long been regarded as one of the most potentially destabilising impacts of the climate crisis. The risks were underlined last month when the U.N.-backed Intergovernmental Panel on Climate Change released a landmark report on oceans that said sea levels could rise by one metre (3.3 ft) by 2100 -- ten times the rate in the 20th century -- if carbon emissions keep climbing.

Last week, Grammarly secured $90 million in funding for its artificial intelligence–based grammar and writing tools. But this specific case drew my attention because amidst all the hype and confusion surrounding artificial intelligence, I believe that Grammarly is solving a real problem. Understanding and processing natural language are among the most challenging areas of AI. Many companies have engaged in ambitious AI-based language projects. But a considerable number of them have failed miserably for not having considered the limits of current AI technologies. Meanwhile, Grammarly has found a niche suitable for the narrow capabilities of deep learning, the current bleeding edge of AI.

Recent innovations in agile aerospace have created unique offerings in high cadence satellite imagery. While this is of immense interest to imagery analysts, a significant portion of GIS professionals and geo-data scientists work less with raster data (AKA imagery) and more with point and vector data. Planet operates the world's largest constellation of earth observation satellites providing near-daily coverage of the entirety of Earth's landmass. Over the past couple of years, we have been working on bringing computer vision and spatiotemporal analysis to market to enable access and data transformations on this rich imagery archive. We recently announced the general availability of our analytic feeds and the launch of our building change detection analytics in private beta (sign up for info here).

The field of precision medicine has latched upon what may well be the Holy Grail in the fight against cancer. When big data are utilized by teams of pathologists, data can be incredibly helpful, but when the right data are comprehensively analyzed by artificial intelligence (AI)-powered models, the data can be downright lifesaving. Predictive Oncology (NASDAQ: POAI) (POAI Profile) is in an enviable position in the precision-medicine industry due to its incredibly rich data set of more than 150,000 clinically validated cases on its molecular information platform, with 30,000-plus specific to ovarian cancer. The company is leveraging this unique database through Artificial Intelligence to provide the actionable insights needed to drive pharma R&D programs and improve patient outcomes. A data asset like this typically takes at least five years to fully validate and most competitors are only in the early stages of the process.

Using artificial intelligence to analyze huge stores of information known as big data boosts the effectiveness of cancer diagnosis and treatment. In 2014, three years after the artificial intelligence system that IBM calls Watson thrashed two humans in a game of "Jeopardy!", the company began selling Watson's services as a virtual oncologist, an intelligent machine that would look at patient records and make treatment recommendations. Some predicted that Watson would clobber oncologists for much the same reason it bested game show contestants --it could retain more data than any person. Before recommending treatment, it could "remember" every word from every cancer study ever published and consider every finding that applied to a particular patient. In reality, according to a 2017 investigation published by the news website Stat, Watson struggled "with the basic step of learning about different kinds of cancers" and disappointed many of the hospitals and practices that purchased its services, sometimes by recommending "unsafe and incorrect" cancer treatments.