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Big Data 10th January 2017 - 5 min read

How Machine Learning and Big Data Drive the Bottom Line

By Femi Ajayi
AI means business, and companies leveraging these technologies will reap the benefits in a data-driven economy

2016 has been a crucial year for the growth of Artificial Intelligence (AI), brought about by a combination of two decisive factors; The public demand for Open Data means there are more freely available datasets than ever before, and the on-going trend towards cheaper and more efficient computing powerprovides the tools necessary to process this data in ever more efficient ways.

This heightened interest in AI is also reflected in the sheer volume of popular fiction works such as Westworld, Humans and Ex Machina, which deal with the moral dilemmas of autonomous robots and thinking machines. Yes as much as we’re fascinated by these (as yet) fictional scenarios, many still struggle to grasp exactly how this technology will – and in many cases already does – affect our everyday lives.

For businesses this has become an imperative, however, and we have seen the focus of Big Data become much more commercially-oriented, centring on managing, measuring and monetizing so-called information assets. To secure an advantage in this data-driven landscape, organisations must develop real world solutions and applications with big data analytics that impact their bottom line.

AI Helping SMEs

The CRM industry, for instance, has taken to artificial intelligence in a big way over the past year, with companies such as Salesforce, Oracle and Base developing tools to drive sales interactions through built-in intelligence. Personalization is one way in which that translates into tangible commercial impact, as it enables companies to scale their services without incurring prohibitive costs or compromising quality.

Fashion service Thread has done this by leveraging machine learning to offer a ‘personal stylist’ service for their customers. The process combines input from professional stylists with an algorithm that trawls through around a quarter-million products in the company’s partner catalogue to provide recommendations, which users then rate – thus helping the algorithm learn and adapt continuously. This combination of AI and human curation is especially important in areas such as fashion, which require a lot of fine tuning due to nuances of taste, and allows companies to provide premium personalized services without the associated costs, meaning they can punch well above their weight.

The Big Four

Yet while AI does offer tangible benefits for SMEs, most of the significant advances in the field are still driven by the so-called “Big Four” (Apple, Facebook, Google and Microsoft). This is mostly due to the sheer amount of resources required to develop effective AI, and the limited availability of top-level Data Science experts in the field. Yet unlike other areas of R&D – where the arms race mentality leads to a climate of protectionism and secrecy – here we see a consensus emerging that there is a need to open source and share findings in order to advance developments and feed the broader ecosystem. Even Apple broke its rule of secrecy by allowing its AI team to publish research papers on the subject for the first time recently.

Facebook’s Head of AI research, Yann LeCun confirmed that the technology will form the “backbone of many of the most innovative apps and services of tomorrow,” and this is reflected in the company’s 10-year roadmap, which places it as one of their core pillars for development. In 2016 Mark Zuckerberg publicly set himself the challenge of building a simple AI to run his home, yet after spending around 100 hours building “Jarvis” he admitted that the project would require a lot more resourcing in order to succeed. AI, he concluded, “is both closer and farther off than we imagine,” but he remains convinced that within a decade we will have artificial systems that can read senses more accurately than the human nervous system.

Google – which invested more in AI than any other single company nowadays – is also keen on baking machine learning technology into every facet of its business. And while initiatives such as Waymo (its recently rebranded self-driving car project) and AlphaGo (DeepMind’s software which beat the Go World Champion) continue to capture the public imagination, the impact of building AI into its G Suite will arguably be much greater, affecting billions of people around the world who use applications such as Gmail every day. Furthermore, DeepMind’s technology has already started yielding commercial applications. In July the company announced that it had significantly reduced the amount of electricity needed to cool Google data centres by feeding the AI its operation logs and optimising the process by running it repeatedly in a simulation. There are now plans to apply this methodology in areas such as healthcare, clean water, and large-scale energy infrastructure.

Microsoft’s main area of focus, meanwhile, has been in conversational computing, which offers a broad range of practical business applications. The Microsoft Bot Framework added a suite of tools to make it easier for its developer community (currently 67,000 strong) to develop bots, and it made Cortana Devices SDK available to hardware manufacturers who want to build smarter capabilities into their devices. This all points to an imminent release of a standalone intelligent agent, which could compete with Amazon’s Echo.

The New Players

Although the Big Four are expected to continue leading in the AI space, there are companies keen to join that particular club. One obvious contender is of course Amazon, which transformed itself from an online bookseller into a company that not only has started making significant moves in the IoT sphere, but also dominates the world of cloud computing.

Uber is also looking to expand and pivot beyond ride-hailing into AI-led areas such as self-driving cars and trucks, and their Chief Product Officer Jeff Holden has recently said there will be step-function changes in artificial intelligence that will affect business models and business opportunities. The company is now in the process of setting up a lab – similar to Google Brain or Facebook’s FAIR lab – to explore a broad range of AI and its applications, incorporating Deep neural network technology pioneered by recently acquired start-up Geometric Intelligence.

Looking Ahead

What emerges from the variety of AI innovations we’ve seen in 2016 is a broad trend towards customer-centric data driven structures. This means using AI to improve customer experience in an increasingly seamless way. The widespread adoption of IoT connected devices will only add to this trend, as we’re expected to have around 34 billion such devices in our homes by 2020. That brings enormous opportunities, but also fresh challenges as companies and their users will need to decide on acceptable parameters for collecting, processing and leveraging the vast amounts of contextual and potentially sensitive data these devices will produce. So, quite apart from robots and replicants, 2017 is shaping up to be a very exciting year for AI.

To find out how machine learning and AI could make your business more profitable as well as reduce costs, get in touch with our expert team

Femi Ajayi
By Femi Ajayi
5 min read

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