Key trends in machine learning and AI

Key trends in machine learning and AI

Key trends in machine learning and AI
You can hardly talk to a technology executive or developer today without talking about artificial intelligence, machine learning or bots. Madrona recently hosted a conference on ML and AI, bringing together some of the biggest technology companies and innovative startups in the Intelligent Application ecosystem.

One of the key themes for the event emerged from a survey of the attendees. Everybody who responded to the survey said that ML is either important or very important to their company and industry.

However, more than half of the respondents said their organizations did not have adequate expertise in ML to be able to do what they need to do.

Here are the other top five takeaways from the conversations at the summit.

Every application is going to be an intelligent application

If your company isn’t using machine learning to detect anomalies, recommend products or predict churn, you will start doing it soon. Because of the rapid generation of new data, availability of massive amounts of compute power and ease of use of new ML platforms (whether it is from large technology companies like Amazon, Google and Microsoft or from startups like Dato), we expect to see more and more applications that generate real-time predictions and continuously get better over time. Of the 100 early-stage startups we have met in the last six months, 90+ percent of them are already planning to use ML to deliver a better experience for their customers.

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Intelligent apps are built on innovations in micro-intelligence and middle-ware services

Companies today fall into two categories (broadly): those that are building some form of ML/AI technology or those that are using ML/AI technologies in their applications and services. There is a tremendous amount of innovation happening in the building block services (aka, middle-ware services) that include both data preparation services and learning services or models-as-a-service providers.

With the advent of microservices and the ability to seamlessly interface with them through REST APIs, there is an increasing trend for the learning services and ML algorithms to be used and re-used — as opposed to having to be re-written from scratch over and over again.

For example, Algorithmia runs a marketplace for algorithms that any intelligent application can use as needed. Combining these algorithms and models with a specific slice of data (use-case specific within a particular vertical) is what we call micro-intelligence, which can be seamlessly incorporated into applications.

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Trust and transparency are absolutely critical in a world of ML and AI

Several high-profile experiments with ML and AI came into the spotlight in the last year. Examples include Microsoft Tay, Google DeepMind AlphaGo, Facebook M and the increasing number of chatbots of all kinds. The rise of natural user interfaces (voice, chat and vision) provide very interesting options and opportunities for us as human beings to interact with virtual assistants (Apple Siri, Amazon Alexa, Microsoft Cortana and Viv).

There are also some more troubling examples of how we interact with artificial intelligences.

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