Machine Learning Digest is a curated weekly news overview for those who are concerned about the Machine Learning development across a spectrum of industries. It provides brief summaries and links to articles and news, describing the most remarkable events in the ML sphere. Learn about the latest news in machine learning development.
Shaping the future of work in a digital era, many companies pay attention to the “AI First” culture.
When machine learning was introduced 70 years ago, the basis for its development was the evidence of learning dynamics in our brain.
Companies in the AI race: ready, set, stop?
WRAL TechWire on August 7, 2019
Shaping the future of work in a digital era, many companies pay attention to the “AI First” culture. The implementation of AI tools in business processes allows driving better customer engagements, boosts rates of innovation and leads to higher competitiveness.
Although the list of AI advantages seems to be endless, according to IDC, only a quarter of firms using AI have a company-wide strategy. The main obstacles to the AI integration are extra costs, the lack of skilled employees and bias in the data. However, half of the respondents see AI as a priority, the study says.
Organizations worldwide must evaluate their vision and transform their people, processes, technology, and data readiness to unleash the power of AI and thrive in the digital era.
Ritu Jyoti, IDC’s program vice president for Artificial Intelligence Strategies
Meet Julia, the top programming language for ML
Appinventiv on August 8, 2019
If the next technical revolution takes place, artificial intelligence and machine learning will lead it. While the technology is filling the market, and Siri, setting the alarm and advising the fastest way to the destination, does not surprise, one of the topical issues among IT experts is how to integrate machine learning into software.
When it comes to languages behind the ML development, the latest one has emerged out as an efficient solution for building intelligent apps, and it is Julia. Released in 2012, this open-source language is used for performing numerical analysis and high-end computations. In addition, Julia enables developers to enjoy the speed of C with the dynamism of Ruby, usability of Python, statistical ability of R, and the mathematical power of MATLAB.
Indeed, AI implementation, despite its high cost, has obvious benefits. The average time spent by staff on data gathering and management, is 12,5% of the whole workday but it can be saved by automating this process through ML. Furthermore, with the help of ML, Amazon shortened the ‘Click-to-ship’ time by 225%.
The bridge between neuroscience and AI is rebuilt
Science Daily on August 9, 2019
This news will definitely appeal to the fans of the “Doctor Who” series, ML developers and everybody who believes in the power of science.
When machine learning was introduced 70 years ago, the basis for its development was the evidence of learning dynamics in our brain. In a recent article, published in the Scientific Reports journal, the researchers of the Bar-Ilan University rebuilt the bridge between neuroscience and advanced artificial intelligence algorithms and revealed its tech potential that has not been used effectively for 70 years.
The scientists have developed a new kind of ultrafast AI algorithm, based on the very slow brain dynamics. The study demonstrates that for small and large networks, ultrafast learning rates are surprisingly similar. Therefore, referring to the researchers, “the disadvantage of the complicated brain’s learning scheme is actually an advantage.”
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