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Machine Learning: The Brains Behind AI

Companies need to get both right before implementation

17Mar

When the average person on the street hears the words Artificial Intelligence, they usually think sentient robots coming to take their job, and potentially their life. Which is understandable, partly because films like Terminator have conditioned us to think like that, and partly because it could well be true. Stephen Hawking and Elon Musk say it might happen, and they’re very rarely wrong about anything. When people hear Machine Learning on the other hand, the tendency is not so much to grab a weapon and hide under the bed until the robot apocalypse comes. It’s to express a healthy reverence for how such algorithms will benefit technology and make our lives easier.

The definition of AI is a broad one. According to Ram Sriharsha, senior architect and machine learning expert at Big Data Hadoop distribution company Hortonworks, ‘Machine Learning is a subset of AI. AI is being able to communicate, being able to plan and reason and take actions. It's also being able to learn, and that's where machine learning comes in.’ Fundamentally, Machine Learning is algorithms that teach a computer to search for certain answers in datasets by itself, and discover patterns that can help regularly improve performance and behaviors. It sits at the heart of AI, which then incorporates other elements, such as natural language processing and understanding, to bring it closer to mimicking human intelligence.

The ultimate goal of big data, and technology in general, is that it will eventually teach itself. While traditional analytics tools are limited by their inability to deal with data past a certain volume and the need for humans to specify program execution, machine learning can process and analyze the volume, velocity and variety of both structured and unstructured data in the way needed for big data to reach its full potential.

Machine Learning algorithms are already playing a part in many every day technologies, including the speech recognition by Apple’s Siri and Facebook’s controversial facial recognition technology. They are also the key technology behind Google’s self-driving cars. The major tech companies are all investing heavily in machine learning as part of their various AI drives. Google CEO Sundar Pichai says that, ‘Machine learning is a core, transformative way by which we’re re-thinking about how we’re doing everything. We are thoughtfully applying it across all our products, be it search, ads, YouTube, or Play. And we’re in early days, but you will see us — in a systematic way — apply machine learning in all these areas.’ In terms of the consumer market, the advantages for making life easier are clear. For businesses, machine learning is vital for sensing and reacting to dynamic, distributed phenomena in such a way that guides forward-looking business decisions.

The world may not be ready for AI quite yet, but businesses certainly need to prepare for machine learning. Big data has promised much since its emergence as a buzzword, and you may think it’s delivered, but machine learning should leave any organizational gains you may have made using data look like specs of dust in comparison.

You can learn more about how machine learning can impact your organization at the Machine Learning Innovation Summit, taking place in San Francisco this June 8–9.

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