October 24, 2018
Top AI and Machine Learning Trends of 2018
We've come a long way since the term artificial intelligence (AI) was coined by AI luminary John McCarthy at Dartmouth in 1955. Sixty-three years later, AI is transforming healthcare, fintech, and other industries across the spectrum. While the quest for a truly humanlike AI continues, advancements in big data and machine learning (ML) have helped AI go mainstream. In this article we'll look at the top AI and ML trends of 2018.
According to Accenture, the U.S. AI healthcare market is projected to reach $6.6 billion by 2021--a compound annual growth rate (CAGR) of 40 percent.
Medical imaging and diagnostic companies are fueling much of AI's growth in health tech. For example, Arterys, a cloud-based AI assistant for radiologists, received FDA clearance for analyzing images of lung and liver tumors with its Oncology AI suite in February 2018.
Personalized healthcare and precision medicine have also been making strides thanks to AI. In August, researchers at the University of Singapore used AI to identify and optimize combination drug treatments for myeloma, a type of blood cancer.
From asset management and fundraising to loans and mobile payments, fintech has already proven itself to be a disruptive technology in its own right. AI is helping fintech take these core competencies to the next level.
This goes beyond the image-recognition tech that lets you deposit checks to your bank via your preferred mobile device. Last October saw the release of the first AI-powered ETF (exchange traded fund), and Elsen is helping large financial institutions process big data. There's also Quantopian, a growing community of quantitative traders looking to test and share trading algorithms, some of which are powered by ML.
From oil and gas giants to renewable green tech, the energy industry produces a lot of data. AI is perfect for processing large data sets and producing actionable insights that can help both energy producers and consumers better utilize the electrical grid.
For instance, the energy storage optimizer Athena processes 400 megabytes of energy data per minute, across more than 800 energy storage systems, to streamline the timing of energy use, helping its customers save $8 million annually.
In the home, smart thermostat Nest reduces energy consumption by adapting to the habits of the home's occupants. Abroad, Great Britain's National Grid and Google's DeepMind are in talks to team up to streamline the U.K.'s electrical grid.
According to the latest market research report by Technavio, the global enterprise AI market is predicted to post a CAGR of 45 percent from 2018 to 2022. Technavio credits much of this growth to the rise of businesses using customer-service chatbots.
ML solutions for processing big data are another reason for this substantial growth. Tech giants such as Alphabet, Amazon, IBM, Intel, and Microsoft are leading the charge, providing and/or supporting many of the underlying AI frameworks and tools that have brought ML to the masses. Google has DeepMind, IBM has Watson, and Amazon Web Services combines with Apache Spark to help enterprises deal with large volumes of data.
Retail continues to be a hotbed of innovation in the AI space. As mentioned above, retailers are reducing customer service overhead with chatbots; they are also using predictive analytics to optimize product pricing and building customer personae from treasure troves of data.
Of course, AI in retail has become so commonplace it's likely you already knew all that. What's new in more recent years, is how businesses have gotten more creative as the technology has matured.
You've heard of chatbots, but what about robots in physical retail spaces boosting foot traffic by as much as 70 percent? Or Kairos, the AI that uses facial recognition to inform store associates about a customer's preferences as soon as he or she walks through the door?
You know you're living in the future when multinational conglomerate SoftBank is in talks to invest up to $750 million in Zume, a robot-operated pizza delivery service.
6. Software Development
Coders are always looking for higher levels of abstraction to increase programmer productivity. It's why the latest software development tools, libraries, and frameworks tout how they streamline and simplify the development process.
The ultimate level of abstraction may one day be to let the programs write themselves. But until then, there are plenty of AI-powered tools that make things a little easier for developers.
Google's bug-prediction tool has long used ML algorithms and statistical analysis to identify flawed code. DeepCode takes things a step further, serving as a Grammarly for programmers--crawling their public and private GitHub repositories, identifying problems, and offering solutions.
From digital assistants to self-driving cars, and beyond
It's 2018, and AI is closer to science fiction than ever before. Exciting news and observations that didn't quite make it onto the above list of top trends include:
- Digital assistants such as Alexa, Cortana, and Siri can be found in the homes of millions across the globe.
- Google Duplex's big reveal at I/O 2018 demonstrated how far NLP (natural language processing) has come; its digital assistant easily booked an appointment at a hair salon with a voice that sounded so human it even included vocal cues such as "mm-hmm" and "um."
- Driverless cars aren't just for Google and Waymo anymore. GM, Daimler (Mercedes-Benz parent company), BMW, and Ford are just some of the auto manufacturers actively developing the technology.
- Manufacturing automation is also benefiting from advances in AI. FANUC Robotics Learning Vibration Control (LVC) Software can be used to accelerate deep learning in robots on assembly lines for specific tasks.
In this article, I focused on how AI was transforming six key sectors: health, fintech, energy, enterprise, retail, and software development. This list barely scratched the surface of all the ways AI has begun to transform our world.
This article originally appeared in Upwork.