SparkBeyond announced the availability of its data science platform for supervised machine learning, SparkBeyond Discovery, in the Microsoft Azure Marketplace, an online store providing applications and services for use on Azure. SparkBeyond customers can now take advantage of the productive and trusted Azure cloud platform, with streamlined deployment and management. SparkBeyond Discovery is a data science platform for supervised machine learning that helps data professionals save time, deepen their understanding of the problem space and improve model performance by automating feature discovery in complex data. The platform discovers known and unknown features in data that give information about a target to save data professionals time in feature engineering, and to maximize data value through applying a wide range of aggregations. The platform finds expressive, interpretable and concise features ranking them based on their predictive power and then applies AutoML to build glass-box models.

Despite the fact that AI has been a hot issue for at least a decade, there are still barriers to its adoption by organisations. According to a Deloitte report, 40% of businesses believe AI technology and skills are too costly. No-code AI refers to a subgroup of artificial intelligence that aims to make AI more approachable to the general consumers. To implement AI and machine learning algorithms, no-code AI implies employing a no-code development platform with a graphical, code-free, and typically drag-and-drop interface. With no-coding AI, non-technical individuals can quickly categorise, assess, and create accurate models to make projections.

Despite the fact that AI has been a hot issue for at least a decade, there are still barriers to its adoption by organisations. According to a Deloitte report, 40% of businesses believe AI technology and skills are too costly. No-code AI refers to a subgroup of artificial intelligence that aims to make AI more approachable to the general consumers. To implement AI and machine learning algorithms, no-code AI implies employing a no-code development platform with a graphical, code-free, and typically drag-and-drop interface. With no-coding AI, non-technical individuals can quickly categorise, assess, and create accurate models to make projections.

The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. SparkBeyond, a company that helps analysts use AI to generate new answers to business problems without requiring any code, today has released its product SparkBeyond Discovery. The company aims to automate the job of a data scientist. Typically, a data scientist looking to solve a problem may be able to generate and test 10 or more hypotheses a day. With SparkBeyond's machine, millions of hypotheses can be generated per minute from the data it leverages from the open web and a client's internal data, the company says.

The Covid-19 pandemic has revealed to us a very inconvenient truth: Regardless of all the technological advances of the last century and a half, our lives can be stopped and destroyed suddenly and unexpectedly by an invisible plague. The excitement over the most recent technological breakthrough--artificial intelligence or deep learning--has met the grim reality of our inability to adequately prepare for, manage, and overcome society's most important and consequential challenges. While Google's DeepMind set itself up to "solve intelligence," i.e., to advance the state of AI and then use the hoped-for "superior intelligence" to solve humanity's challenges, Sagie Davidovich and Ron Karidi co-founded SparkBeyond 7 years ago "to harness the world's collective intelligence in order to solve the world's toughest challenges." They aimed to use existing AI, algorithms, and knowledge to advance humanity's problem-solving capabilities. Most important, SparkBeyond wanted to go beyond the typical use of AI which is basically an extension and an upgrade of what has been called "predictive analytics" before the 2010s.

Building on a pilot partnership with an artificial intelligence company it launched last year, Baker McKenzie is upping its bet that machine learning and data-driven analytics will benefit the firm and its clients. Baker McKenzie, which first teamed up with AI-powered platform SparkBeyond in October, is now entering into a three-year exclusive contract with the company and building a new 11-person team within the firm to leverage the technology for internal and client-facing projects, the global firm said Monday. The firm plans to hire two co-founders to build out and lead the new team alongside London-based partner Ben Allgrove, who is Baker McKenzie's global head of research and development. The firm said candidates for the roles - which are now open for applications - should be "steeped in legal innovation." "Five years ago our industry was flooded with hype about AI disruption," Allgrove said in a statement.

SparkBeyond, an AI-powered problem-solving platform that augments and accelerates the generation of novel insights out of data and knowledge, alongside Baker McKenzie, a leading multinational law firm, announced a market-first collaboration that will apply SparkBeyond's technology to reimagine legal client services in the future. Baker McKenzie will launch its new global innovation arm, Reinvent, and apply SparkBeyond's AI to predict which services clients will require from law firms, explore unforeseen drivers of client demand and learn how to evolve its business to accommodate those needs. The partnership also aims to reimagine traditional law practices and pave new paths for tens of thousands of firms across the globe. "The legal sector is on a new path for disruption and innovation and our partnership with SparkBeyond will turbo-charge the evolution in our business," said Ben Allgrove, Partner at Baker McKenzie. "Understanding the drivers and root causes driving future client demand will allow us unparalleled insights and the ability to shape the future of our business to create additional value across the legal, tax, and compliance functions in the future."

In the continuing theme of higher level tools to improve developing useful applications, today we'll visit feature engineering in a changing environment. Artificial intelligence (AI) is increasingly used to analyze data, and deep learning (DL) is one of the more complex aspects of AI. In multiple forums, I've discussed the need to move past heavy reliance on not just pure coding, but even past the basic frameworks discussed by DL programmers. One of the keys to the complexity is figuring out the right data attributes, or features, which matter to any system. As tricky as that is the first time, it needs to be a repeatable process, as environments change, and systems must change with them. Defining the initial feature set is important, but it's not the end of the game.

Recent surveys, studies, forecasts and other quantitative assessments of AI highlight the role AI plays in fighting the Coronavirus, the business impact of AI, and what the American public feels about it. UC San Diego Health developed and applied an artificial intelligence algorithm to more than 2,000 lung X-ray images, helping radiologists more quickly identify signs of early pneumonia in Covid-19 patients [Becker's Hospital Review] Mayo Clinic teamed up with the state's health department to create an artificial intelligence-powered tool that can identify zones of greater Covid-19 transmission in southern Minnesota [Becker's Hospital Review] The FluSense model, developed by researchers at University of Massachusetts Amherst, was tested in campus clinic waiting rooms. The AI platform was able to analyze coughing sounds and crowd size collected by the handheld device in real-time, then use that data to accurately predict daily illness rates in each clinic [Becker's Hospital Review] The Rambam Hospital in Haifa, Israel, has begun a clinical trial of Cordio Medical's app-based AI system that analyzes speech to diagnose and remotely monitor Covid-19 patients [VentureBeat] Kentucky-based Baptist Health is using an AI platform from remote-patient-monitoring startup Current Health Ltd. to track about 20 Covid-19 patients [WSJ] AI startup SparkBeyond will assist Argentina in looking at how the country can allow citizens to return to work and minimize economic impact. The platform will use data from the Argentinian ministry of health, which aggregates travel, demographic and employment data for each citizen, then integrates hundreds of external data sources to create a wider picture of the situation. It is an area where any country, even countries as big as China and the United States, will find it challenging to achieve the necessary scale of data--from tens to hundreds of millions of humans--to train machine-learning applications that generate robust insights into health and disease.

Six years ago, Sagie Davidovich and Ron Karidi wanted to see if they could find a way to harness all of humanity's collective intelligence using artificial intelligence (AI). Now, their co-founded startup, SparkBeyond, is making waves in AI, using their platform to ask the questions about data we've never thought of asking before. SparkBeyond's problem-solving platform takes data provided by its customers and partners, and analyzes it, using open-source algorithms to look for unique patterns and identifiers. The company is "vertically agnostic," SparkBeyond's Senior Data Scientist, Ryan Grosso, told AI Trends. The company has partnered with experts in insurance, financial services, pharmaceutical, and retail sectors among others.