How Machine Learning and Big Data Drive the Bottom Line

How Machine Learning and Big Data Drive the Bottom Line

How Machine Learning and Big Data Drive the Bottom Line

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 power provides 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 Wetsworld, HumansandEx 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.

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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.

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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.

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