The future of machine learning SwiftERM - Retail Development

The future of machine learning: 5 trends to watch around algorithms, cloud, IoT, and big data. No one can predict the future of technology with 100 per cent accuracy. But these four pillars are certainly at the forefront of innovation in the years ahead. Speaking at a machine learning and artificial intelligence event hosted by Madrona Venture Group in Seattle on Wednesday, Joseph Sirosh, corporate VP of the Data Group at Microsoft, outlined five trends to watch in a world he described as “ACID”: Algorithm, Cloud, IoT, and Data.

“We live in a time of great change in computing, where unreasonable effectiveness of algorithms, cloud, IoT, and data are changing how applications are built, period,” he said. “Even if you are on the right track, if you don’t hop on this bandwagon and actually build things and deploy them and take advantage of their strength, you won’t be very effective.”

Sirosh offered up numerous examples of how entrepreneurs and companies are utilizing these new technologies to improve business processes, from farmers in Japan tracking cow steps to monitor health more effectively, to companies like Uber and Airbnb that rely heavily on algorithms.

He also noted that there is an incredible amount of data being produced from sensors and other devices, but added that “data is only usable through analytics.”

Here’s a recap of the trends that Sirosh discussed:

“Every business is an algorithmic business”

Sirosh said that the manual management of business processes will become “antiquated” as technology starts managing this at scale. “Everything at scale in this world will be managed by algorithms and data,” he said. Sirosh added that there’s a need for effective platforms for managing these algorithmic businesses. He also talked about the importance of programming languages like “R.”

“Cloud-hosted intelligence”

While growing up in India, Sirosh said he would buy cloth by the yard and have a tailor measure his body size. He’d then receive fitted clothes a few weeks later. Today, he noted how this isn’t common because the mass manufacturing of clothing has become so automated and cheap.

“Analytics and data science today are like tailoring 40 years ago … it takes a long time and a tremendous amount of effort,” he said. “… A big part of the future of machine learning is going to be like clothing today. When the effort to build and deploy machine learning models becomes a lot less — when you can ‘mass manufacture’ it — then the data to do that becomes widely available in the cloud. We’re going to have a cloud platform that’s like a department store.”

Sirosh said that as the effort to build and deploy machine learning models becomes easier, we’ll have huge app store-like marketplaces — to his analogy, “department stores” — for APIs and applications that can be used to build software to help automate more processes.

“Mission-critical intelligent apps”

Sirosh used DocuSign as a good example of how machine learning and algorithms are being used to develop “mission-critical intelligent apps.” He said DocuSign’s customers have seen a 93 per cent reduction in the time it takes to close a contract.

“Industrial Internet of Things”

Sirosh said that consumer IoT — think Fitbit, Microsoft Band, etc. — gets most of the attention today, but industrial IoT should also be noted. “What happens when 50 billion machines become connected?” he asked. Everything from hospitals to factories to highways can be improved with IoT technology, Sirosh said. Even football statistics and energy grid management will change with IoT advancements, he added.

“Ultra-scale analytics”

Sirosh talked about the concept of “data lakes.” “You can ingest information from multiple data streams in a lake,” he said. “But who are the swimmers?” The swimmers, he said, are tools like Hadoop, Apache Spark, NoSQL, and other applications that help crunch numbers at scale and provide actionable insights.

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