SAN FRANCISCO, Jan. 22, 2020 /PRNewswire/ -- Vectorspace AI (VXV) announces datasets that power data engineering, machine learning (ML) and artificial intelligence (AI) systems. Vectorspace AI alternative datasets are designed for predicting unique hidden relationships between objects including current and future price correlations between equities.

Vectorspace AI enables data, ML and Natural Language Processing/Understanding (NLP/NLU) engineers and scientists to save time by testing a hypothesis or running experiments faster to achieve an improvement in bottom line revenue and information discovery. Vectorspace AI datasets underpin most of ML and AI by improving returns from R&D divisions of any company in discovering hidden relationships in drug development.

"We are happy to be working with Vectorspace AI based on their most recent collaboration with us based on the article we published titled 'Generating and visualizing alpha with Vectorspace AI datasets and Canvas'. They represent the tip of the spear when it comes to advances in machine learning and artificial intelligence. Our customers and partners will certainly benefit from our continued joint development efforts in ML and AI," Shaun McGough, Product Engineering, Elastic.

Increasing the speed of discovery in every industry remains the aim of Vectorspace AI, along with a particular goal which relates to engineering machines to trade information with one another, connected to exchanging and transacting data in a way that minimizes a selected loss function. Data vendors such as Neudata.co, asset management companies and hedge funds including WorldQuant, use Vectorspace AI datasets to improve and protect 'alpha'.

Limited releases of Vectorspace AI datasets will be available in partnership with Amazon and Microsoft.

About Vectorspace AI (vectorspace.ai)

Vectorspace AI focuses on context-controlled NLP/NLU (Natural Language Processing/Understanding) and feature engineering for hidden relationship detection in data for the purpose of powering advanced approaches in Artificial Intelligence (AI) and Machine Learning (ML). Our platform powers research groups, data vendors, funds and institutions by generating on-demand NLP/NLU correlation matrix datasets. We are particularly interested in how we can get machines to trade information with one another or exchange and transact data in a way that minimizes a selected loss function. Our objective is to enable any group analyzing data to save time by testing a hypothesis or running experiments with higher throughput. This can increase the speed of innovation, novel scientific breakthroughs and discoveries. For a little more on who we are, see our latest reddit AMA on r/AskScience or join our 24 hour communication channel here. Vectorspace AI offers NLP/NLU services and alternative datasets consisting of correlation matrices, context-controlled sentiment scoring, and other automatically engineered feature attributes. These services are available utilizing the VXV token and VXV wallet-enabled API. Vectorspace AI is a spin-off from Lawrence Berkeley National Laboratory (LBNL) and the U.S. Dept. of Energy (DOE). The team holds patents in the area of hidden relationship discovery.

SOURCE Vectorspace AI

vectorspace.ai

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Vectorspace AI Datasets are Now Available to Power Machine Learning (ML) and Artificial Intelligence (AI) Systems in Collaboration with Elastic -...

Jenkins Creator Launches Startup To Speed Software Testing with Machine Learning

Kohsuke Kawaguchi, creator of the open source Jenkins continuous integration/continuous delivery (CI/CD) server, and Harpreet Singh, former head of the product group at Atlassian, have launched a startup that's using machine learning (ML) to speed up the software testing process.

Their new company, Launchable, which emerged from stealth mode on Thursday, is developing a software-as-a-service (SaaS) product with the ability to predict the likelihood of a failure for each test case, given a change in the source code. The service will use ML to extract insights from the massive and growing amount of data generated by the increasingly automated software development process to make its predictions.

"As a developer, I've seen this problem of slow feedback from tests first-hand," Kawaguchi told ADTmag. "And as the guy who drove automation in the industry with Jenkins, it seemed to me that we could make use of all that data the automation is generating by applying machine learning to the problem. I thought we should be able to train the machine on the model and apply quantifiable metrics, instead of relying on human experience and gut instinct. We believe we can predict, with meaningful accuracy, what tests are more likely to catch a regression, given what has changed, and that translates to faster feedback to developers."

The strategy here is to run only a meaningful subset of tests, in the order that minimizes the feedback delay.

Kawaguchi (known as "KK") and Singh worked together at CloudBees, the chief commercial supporter of Jenkins. Singh left that company in 2018 to serve as GM of Atlassian's Bitbucket cloud group. Kawaguchi became an elite developer and architect at CloudBees, and he's been a part of the community throughout the evolution of this technology. His departure from the company was amicable: Its CEO and co-founder Sacha Labourey is an investor in the startup, and Kawaguchi will continue to be involved with the Jenkins community, he said.

Software testing has been a passion of Kawaguchi's since his days at Sun Microsystems, where he developed Jenkins as a fork of the Hudson CI server in 2011. Singh also worked at Sun and served as the first product manager for Hudson before working on Jenkins. They will serve as co-CEOs of the new company. They reportedly snagged $3.2 million in seed funding to get the ball rolling.

"KK and I got to talking about how the way we test now impacts developer productivity, and how machine learning could be used to address the problem," Singh said. "And then we started talking about doing a startup. We sat next to each other at CloudBees for eight years; it was an opportunity I couldn't pass up."

An ML engine is at the heart of the Launchable SaaS, but it's really all about the data, Singh said.

"We saw all these sales and marketing guys making data-driven decisions -- even more than the engineers, which was kind of embarrassing," Singh said. "So it became a mission for us to change that. It's kind of our north star."

The co-execs are currently talking with potential partners and recruiting engineers and data scientists. They offered no hard release date, but they said they expect a version of the Launchable SaaS to become generally available later this year.

Posted by John K. Waters on 01/23/2020 at 11:30 AM

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Jenkins Creator Launches Startup To Speed Software Testing with Machine Learning -- ADTmag - ADT Magazine

Both Millennials and Generation Z can be categorized as digital natives. The way in which they learn reflects that reality. From a learning perspective, a companys learning programs must reflect that also.

Utilizing technologies such as microlearning, which is usually delivered with mobile technology, or machine learning to can engage these individuals in the way they are accustomed to consuming information.

Microlearning is delivering learning in bite-sized pieces. It can take many different forms such an animation or a video. In either case, the information is delivered in a short amount of time; in as little as two to three minutes. In most cases, micro-learning happens on a mobile device or tablet.

When should micro-learning be used?

Think of it as a way to engage employees already on the job. It can be used to deliver quick bits of information that will become immediately relevant to their daily responsibilities. To be more pointed, microlearning is the bridge between formal training and application. At least one study shows after six weeks following a formal training, 85% of the content consumed will have been lost. Microlearning can deliver that information in the interim and can be used at the moment of application.

Microlearning shouldnt be used to replace formal training, but rather as a compliment which makes it perfect for developing and retaining high-quality talent.

Amnesty International piloted a microlearning strategy to launch its global campaign on Human Rights Defenders. The program used the learning approached to build a culture of human rights. It allowed Amnesty to discuss human rights issues in a quick, relevant, and creative manner. As such, learners were taught how to talk to people in everyday life about human rights and human rights defenders.

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Dell has also used the strategy to implement a digital campaign to encourage 14,000 sales representatives around the world to implement elements of its Net Promoter Score methodology. Using mobile technology and personal computers, the company was able to achieve 11% to 19% uptake in desire among sales reps globally.

Machine learning can also be used as a strategy. Machine learning, which is a branch of artificial intelligence, is an application that provides systems the ability to automatically learn and improve from experience without being programmed to do so.

For the purpose of explanation, the example of an AI-controlled multiple-choice test is relevant. If a person taking the test marked an incorrect answer, AI would then give them a question a bit easier to answer. If the question was answered wrong again, AI would follow with a question lower in difficulty level. When the student began to answer questions correctly, the difficulty of the questions would increase. Similarly, a person answering questions correctly would continue to get more difficult questions. This allows the AI to determine what topics the student understands least. In doing so, learning becomes personalized and specific for the student.

But technology isnt the sole basis for disseminating information. Learning programs should also focus on creating more experience opportunities that offer development in either leadership or talent. Those programs should also prioritize retention. Programs such as mentoring and coaching are great examples.

Dipankar Bandyopadhyay led this charge when he was the Vice President of HR Global R&D and Integration Planning Lead Culture & Change Management for the Monsanto Company. Monsanto achieved this through itsGlobal Leadership Program For Experienced Hires.

A couple of years ago, we realized we had a need to supplement our talent pipeline, essentially in our commercial organization and businesses globally really building talent for key leadership roles within the business, which play really critical influence roles and help drive organizational strategy in these areas. With this intention, we created Global Commercial Emerging Leaders Program, Bandyopadhyay said. Essentially, what it does is focus on getting external talent into Monsanto through different industry segments. This allows us to broaden our talent pipeline, bringing in diverse points of view from very different industry segments (i.e., consumer goods, investment banking, the technology space, etc.) The program selects, onboards, assimilates and develops external talent to come into Monsanto.

Microlearning and machine learning are valuable in developing the workforce, but they are not the only ones available. Additionally, its important to note an organization cant simply provide development and walk away. There has to be data and analysis that tracks employee learning success. There also needs to be strategies in place to make sure workers are retaining that knowledge. Otherwise, it is a waste of money.

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Learning that Targets Millennial and Generation Z Workers - HR Exchange Network

Workday co-founder and CEO Aneel Bhusri has been a success. He started the human resources technology company in 2005 and has grown it to a stock market valuation over $40 billion. His own net worth is valued by Forbes in the billions. He sees technology playing a big role in the success of all workers in the future. In particular, machine learning and the blockchain.

"Blockchain is a technology looking for a problem to solve. We found one to solve, which is credentials," Bhusri told the anchors of CNBC's "Squawk Box" from the World Economic Forum in Davos, Switzerland, on Thursday.

"Employees can go from company to company and carry credentials with them in a private network. It can't be edited by an outside source," he said.

The blockchain would prevent the ability of job seekers to lie about their professional and educational histories, which has been a pervasive problem, even up to the level of CEOs, on platforms like LinkedIn and for background-check firms that work with hiring companies. Workday has a partnership with First Advantage for background-screening technology.

"Whatever information you want to carry ... it gives employees power over data," Bhusri said. "Universities can also make sure that diplomas cited by job seekers are real."

The education sector is moving in this direction. One example is Blockcerts, a platform developed at MIT that can be used for creating, issuing, viewing and verifying blockchain-based educational certificates.

While many talk about AI, Bhusri said he prefers to talk about machine learning and how its predictive power will change the job market of the future.

"AI gives people images of the Terminator, and that's not the world we live in. Machine learning lets you make predictions ... sifting through massive amounts of data. ... Humans are great at making judgments."

Bhusri said the predictive power of machine learning will allow humans to then make great judgments, and that process will become important to finding the right job fit.

"AI will predict the right next move in your career," Bhusri said.

The WorkDay CEO is not alone in focusing on this opportunity. IBM has in recent years been exploring ways its artificial intelligence technology, Watson, can lead to a revolution in the human resources department. IBM claims to have technology that can predict when an employee is going to quit, as well as matching employees up to better potential opportunities. Its CEO has claimed that the predictive technology works with up to 95% accuracy.

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Workday CEO says HR company has a blockchain solution that will be key to finding a job - CNBC