Where Machine Learning is Headed

Where Machine Learning is Headed

Today’s world of big data requires technologies such as machine learning to help process and analyze vast amounts of data.

It is no surprise that the demand for machine learning is increasing across different industries: of the 1,600 organizations recently surveyed by MemSQL and O’Reilly Media, 61% most frequently picked Machine Learning / Artificial Intelligence as their company’s most significant data initiative for next year. Additionally, PR Newswire reports that the global machine learning market is expected to grow from $1.41B to $8.81B between 2017 and 2022.

How will companies be using machine learning in the coming years?

More and more startups are using machine learning in the following fields:

Consumer experiences

Companies (tech giants and startups alike) are leveraging machine learning to bring their products and services to the next level.

  • Smarter tech products: Google’s smartphone Pixel 2 uses machine learning in its RAISR (Rapid and accurate image super resolution) camera technology. Trained with real photos, RAISR enhances the sharpness of zoomed-in photos by making intelligent guesses to fill in missing details.

  • Better apps: Neiman Marcus’s “Snap.Find.Shop.” app enables users to take a picture of a product and automatically find and purchase the product or similar products online. This image-recognition software is made possible with machine learning.

  • Personalized customer service: Simplr, founded in 2017, provides customer service solutions to businesses. They use machine learning to automate and personalize customer service emails and chatbots for e-commerce companies. Their solutions are on-demand, precise, and scalable.


With AI and machine learning, companies can now better solve problems, ranging from protecting their data to efficiently creating websites.

  • Cyber-security for businesses: Cylance is a company that uses AI and machine learning to tackle cyber-security problems. Based in Orange County, they help businesses and governments prevent breaches and attacks.

  • IT services: LogDNA, a California-based machine learning startup with $8.4 million raised as of 2018, applies machine learning to IT log data to help organizations detect and address IT problems before they can happen. As a result, companies can cut down on costs of IT repair.

  • E-commerce services: Bookmark is a company that designs and creates personalized websites through machine learning. This service eliminates the need for potentially costly human labor. It is backed with “AiDA” (AI design assistant) technology, which can check competitor websites to help Bookmark optimally design websites for its clients.


In the next 10 years, machine learning is expected to be popular among startups that work with clinic labs and diagnostic companies.

  • Disease detection: Paige.AI (Pathology AI Guidance Engine) pairs machine learning with pathology. This startup helps pathologists process their data, ultimately providing insights that allow for quicker and better disease-detection in patients. Informed decision-making and more accurate predictions will arise as a result.

  • Conception insight: Univfy, based in Los Altos CA, maximizes women’s chances of conception through more accurate predictions of their probability of success. Machine learning is used to analyze patient health data and give personalized reports, helping those who suffer from infertility.

  • Clinical assistance: With $5.5 million raised as of 2018, Bay Labs Inc. is using machine learning in cardiovascular imaging to help clinicians better interpret and perform echocardiograms through algorithms and video collections.

  • Automation of repetitive tasks: CrossChx is an organization that utilizes machine learning to streamline large-scale, repetitive tasks for healthcare companies. Some of these tasks include transferring patient data, processing medical bills, and reporting analytics.


There is plenty of room for growth in agtech, for resources can often be used more efficiently. As a result, many startups are expected to continue appearing in this industry.

  • Soil monitoring: Teralytic, an agtech firm founded in 2016, uses wireless sensors to provide farmers with quality soil data. This allows farmers to increase yields and minimize costs through soil science, internet of things, and machine learning.

  • Energy and water management: Wexus Technologies Inc. is a San Francisco-based startup that makes farms more profitable, efficient, and accident-free. They specialize in using machine learning to detect equipment problems early on to eliminate risk, track data to reduce waste and maximize profits, and save time and labor by automating tasks.

  • Process analysis: Biome Makers is a San Francisco-based startup specialized in providing comprehensive analysis for agricultural and industrial processes. This microbiome startup aims to use vineyard microorganisms to help winemakers grow better vines.

With such a high demand for machine learning, there is also no shortage in investment:

  • McKinsey Global Institute found that nearly 60% of the $8-12 billion investment in AI during 2016 was invested in machine learning.

  • Machine learning patents grew at 34% compound annual growth rate between 2013 and 2017 according to IFI Claims Patent Services.

Alongside plentiful investment, the tech industry will continue to see large amounts of R&D and M&A

Big tech companies like Microsoft, Apple, Tesla, and GE are leading the way with machine learning technology through their own research. For example, GE has been using machine learning to collect data and create “digital twins” of industrial machines (windmills, gas turbines, jet engines), allowing for analysis and replication of machine performance.

As for M&A, a popular data science platform and community called Kaggle was acquired by Google in 2017. Kaggle contains many open-source competitions, datasets, and algorithms to cultivate machine learning education. Now with Kaggle’s resources and community, Google can better bring together data scientists and make further progress in the field of machine learning.

What does this all mean?

The collection of big data provides insights that let companies refine their products and services, optimize their machines and processes, and tackle problems more efficiently than ever. With so many companies working with machine learning, this trending branch of AI will inevitably continue to positively impact industries. Healthcare, consumer experiences, enterprise, and agriculture are all sectors to be on the lookout for in the coming years as more and more tech companies enter these fields.

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