The legal value of XAI can be significant, especially (though by no means exclusively) in mitigating developer and end-user liability.¹ Though it is somewhat early to presume that the perfect information model introduced in The Role of Explainable AI (XAI) in Regulating AI Behavior: Delivery of "Perfect Information"can be viewed as a mature standard, the model does possess the necessary proto qualities and can be reasonably viewed as a proto standard. Therefore, a properly developed XAI is one that possesses, at a minimum, all the attributes of perfect information. And once that parameter is fixed, the XAI is deemed properly developed and ready to provide a variety of risk mitigation benefits. One example of how this can work is in dispositive-centric efforts, including in crafting safe harbors.

Recent surveys, studies, forecasts and other quantitative assessments of the impact and progress of AI highlighted the need to retrain many workers, improving AI's score from F to A on 8th-grade science exam, and the $97.9 billion the AI market will reach in 2023. In the next three years, as many as 120 million workers in the world's 12 largest economies may need to be retrained or reskilled as a result of AI and intelligent automation; only 41% of CEOs surveyed say that they have the people, skills and resources required to execute their business strategies; the time it takes to close a skills gap through training has increased from 3 days on average in 2014 to 36 days in 2018 [IBM] Top drivers for investing in robotics and automation: Reduced cost (80%), improved quality (55%), increased productivity (54%), improved capabilities of robots (54%). L'Oréal's recruiters believe they saved 200 hours of time to hire 80 interns out of a pool of 12,000 candidates, using a chatbot that saves significant time in the early stages of the recruiting process by handling questions from candidates, and Seedlink, AI software that assesses their responses to open-ended interview questions [Forbes] Infusion Software, using a chatbot from LeadsDrift.com, has reduced the number of front-line salespeople who field customer inquiries from 25 to 9 since April and expects to save $1 million a year [Wall Street Journal]

The new HPE ML Ops solution extends the capabilities of the BlueData EPIC container software platform, providing data science teams with on-demand access to containerized environments for distributed AI / ML and analytics. BlueData was acquired by HPE in November 2018 to bolster its AI, analytics, and container offerings, and complements HPE's Hybrid IT solutions and HPE Pointnext Services for enterprise AI deployments. Enterprise AI adoption has more than doubled in the last four years1, and organizations continue to invest significant time and resources in building machine learning and deep learning models for a wide range of AI use cases such as fraud detection, personalized medicine, and predictive customer analytics. However, the biggest challenge faced by technical professionals is operationalizing ML, also known as the "last mile," to successfully deploy and manage these models, and unlock business value. According to Gartner, by 2021, at least 50 percent of machine learning projects will not be fully deployed due to lack of operationalization.2

Lasse Rouhiainen is a best-selling author and international expert on artificial intelligence, disruptive technologies and digital marketing. Finnish in origin but based in Spain, Lasse focuses his work on investigating how companies and society in general can better adapt to, and benefit from, artificial intelligence. Lasse has given keynote presentations, seminars and workshops in more than 16 countries around the world and holds frequent conferences at several universities internationally. He has also provided training to thousands of students and businesses through online e-learning courses. Lasse has been a speaker at renowned seminars such as Mobile World Capital and TEDx, and has worked with top brands and institutions such as Michelin, Össur and the European Union Intellectual Property Office.

Source: Capital IQ; Pitchbook; Deallogic; S&P; McKinsey Global Institute "Overall Europe is behind in private investments in Artificial Intelligence" AI strategy for Europe* * COM(2018)/237 Total VC Investments in Blockchain (2010-2018) • North America: $5,1 billion (Canada: $623 Mill.) • China: $1.5 billion • Europe: $1,2 billion (CH:$414 Mill.&

Researchers at the University of North Carolina's Center for Geospatial Analytics (CGA) are using artificial intelligence (AI) and machine learning (ML) …

Researchers at the University of North Carolina's Center for Geospatial Analytics (CGA) are using artificial intelligence (AI) and machine learning (ML) to help farmers better adapt their crops to changing climates. Speaking to ZDNet, CGA associate director Ranga Raju Vatsavai said his team of researchers has been working in partnership with Lenovo for the last two years to develop AI and ML solutions to help farmers preemptively identify ways to best optimise water and energy -- and ultimately address the threats to food insecurity. "Our area of research is to extract actionable knowledge from the datasets. Food, energy, and water are a good application because the population is going to reach 10 billion by 2050. Right now, we are utilising 70% of fresh water for agriculture," he said.

In this article, we list down 6 trending jobs in machine learning one can apply. Responsibilities: The responsibilities include developing highly scalable classifiers and tools leveraging machine learning, data regression and rule-based models, deep learning, create language models from petabytes of text data in different languages, suggest, collect and synthesize requirements and innovate to create next-generation feature sets. The candidate will work as part of the product team to implement algorithms that power user and developer-facing products reaching out to millions of users, adapt standard machine learning methods to best exploit modern parallel environments. Prerequisites: The candidate must have strong background in one or more of Machine Learning, Artificial Intelligence, Pattern Recognition, Natural Language, Deep Learning, DNNs, large scale Data Mining, experience with scripting languages such as Perl, Python, PHP, and shell scripts, experience with recommendation systems, targeting systems, ranking systems or similar systems, experience with any of Hadoop/Hbase/Pig or MapReduce/Bigtable or R/Matlab/AzureML or similar technologies. Responsibilities: The responsibilities for a Machine Learning Engineer – Lead include building common ML capabilities used across Corporate based on machine learning models, automate and streamline existing processes, procedures, and toolsets.

Humans are now generating an estimated 2.5 quintillion bytes of data every single day, with more data being created in the past two years than in all of human history. Managing this growing flood is complex and the task comes with a high level of responsibility. The 24/7 requirements on business and huge security challenges mean that'manual" management is no longer an option. Particularly when combined together they will let businesses manage and get value from their information more easily, effectively, and with less effort. One technology in particular that is unlocking new levels of value is the autonomous database.

For all of the hype that machine learning has generated over the past few years, there is one question on every budget approver's mind: When will we see our return-on-investment? Organizations will not see an immediate return, according to Valentino Zocca, vice president, data science at Citi and who moderated a panel at the AI in Finance Summit in Midtown Manhattan. "It's a journey, and it takes time." A significant governing factor on the pace of ROI is how the firm organizes its machine-learning resources, added Kamalesh Rao, a senior data scientist at Société Générale and who participated on the panel. Centralized and decentralized approaches each have their strengths and weaknesses. The quickest way to see an ROI would be to embed one or two data scientists into existing data practices, according to Rao. "It might not be the entire organization, but an individual siloed practice that can deliver results on a platform, which can scale quickly," he said.