#Asia 2015’s big leap into machine learning


In other words, this is what we can expect humans won’t be doing in 2016


A few decades ago, artificial intelligence (AI) was just an exciting topic among engineers and developers. In recent years, machine learning has emerged as the ideal outgrowth of Big Data, breathing new life into concepts such as artificial intelligence.

This is how machine learning changed our lives this year and how we expect these advances to impact us in 2016.

2015 – the market shifts

For those unfamiliar with machine learning, or ML to industry folks, it’s a subset of artificial intelligence where computer algorithms can receive new information and learn without supervision. For example, Tesla’s self-driving car made a huge splash in 2015. But machine learning has much more in store than a quick and easy commute.

This year, Google, Amazon, Apple, Facebook and Microsoft demonstrated a serious commitment to machine learning.

Google made its intent clear by giving the company room to grow when it restructured into Alphabet. Always in competition, Apple has shown an interest in hiring machine learning experts with the hopes of better-predicting customer needs.

Amazon has also launched its own machine learning platform with a simpler, yet limited, approach and over the summer, Microsoft unveiled its Cortana analytics suite. These developments, though they are not industry firsts, reflect the presence machine learning will have in our lives.

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Facebook made a key move with its introduction of M, which has proven itself extraordinarily useful as a personal assistant thanks to its ability to perform highly complex tasks. Underneath this capability, however, lies the most important aspect of machine learning: the “human in the loop” model.

2016 – big year for machine learning

Traditionally speaking, computers have been used to enhance the human ability to carry out tasks. Front end users see this the most with features like autocomplete and spell check. With machine learning, Facebook flips this model on its head and uses humans to enhance the work of computers.

The “human in the loop” model is the premise from which machine learning will make the next qualitative leap in computing and define machine learning’s accomplishments in the upcoming year. These leaps are likely to be made on three main fronts: natural language processing, personalisation, and security.

Speaking the same language

Natural language processing, otherwise known as NLP, will become more present in 2016 with the increased use of personal assistants such as M. Look for Facebook to expand its use of M and for its competitors to add humans into their “loops.”

As this process unfolds, doing this will make personal assistants much more capable and accelerate the growth of machine learning itself as developers bridge the gap between human and digital language processing. Beyond personal assistants, NLP will also come into the fore in spoken conversations: Digital operators will become more interactive and fast-paced.

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In more day-to-day terms, NLP will also enable computers to conduct live analysis of plain text, such as those found in emails, Word documents and slideshow presentations. This will lead to features like automatic fact-checking as well as the creation of computerised citations and footnotes.

Even more complex elements that are typically thought of as being exclusively human, such as presentation design and image selection, will become automated. Services will be able to suggest attachments for emails, insert the correct charts into board reports, and turn bullet points into cutting-edge, visual presentations. Moreover, translation services will improve — it will soon be the case that people who do not share a common language will be able to have live, online conversations without the use of a translator, and nearly without miscommunication.

Getting personal

In understanding what you are saying, machine learning will become more capable of comprehending who you are, including making deep assessments about your personality. The more we use digital devices, the more information is captured and the more we learn about ourselves. On the user end, the most immediate change will be more intensely personalised search results. The increase in knowledge about users will also reduce the number of unwanted and irrelevant ads that users are forced to see.

In 2016, the digital world will become much more attuned to your personal world. Your computer will begin to create an assessment of who your best friends are, what your food preferences entail, and even what your mood may be.

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The rise of security

More knowledge about users will also boost security by enhancing methods of authentication and user verification. Behavioural biometrics has already demonstrated how this method can be effective with technology that can create unique user profiles that analyse how users interact with their banking websites, alerting banks to any variation from this behaviour and preventing fraud.

Machine learning will be able to take this same principle and apply it beyond banking: If someone uses your credit card or personal information in a way that does not fit your personality, your credit card company or other security service, will be alerted. This feature can also be applied to device security. For example, if your computer or cell phone runs programmes or communicates with devices that are inconsistent with your profile, you will be alerted. This system will prevent malware, put a stop to trojans, and keep hackers away from your data.

In 2015, the world got a glimpse of the potential of machine learning technology. Today’s best technological minds are dedicated to creating machines that can learn and adapt to our ever-changing world, removing the need for humans where we were previously essential. Though the giant companies have taken the headlines, next year look for smaller, flexible startups to take the development lead as machine learning becomes a bigger part of our everyday lives.

The article 2015’s big leap into machine learning first appeared on Geektime

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