Should You Be Using AI In Your Business?: White Paper

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As well as hosting global AI and Deep Learning events, RE•WORK’s digital content platform is host to an abundance of expert content including podcasts, video playlists, and now white papers.

Our first long form study, ‘Should You Be Using AI In Your Business?' is now available for free download here. This paper explores the application of AI in business, delves into who should be employing these technologies, and hones in on the transformative impact AI is having on every industry.  Research contributions come from leading minds in the field including Ian Goodfellow, Staff Research Scientist at Google Brain, Eli David, Co-Founder and CTO at Deep Instinct, Ankur Handa, Research Scientist at OpenAI, Maggie Mhannah, Data Scientist at Renault Digital, Jörg Bornschien, Research Scientist at DeepMind and many more. Expert opinions from academics, industry leaders, researchers, CEOs, founders and many more are included to comment on the impact of artificial intelligence across multiple industries.

Preview:

AI is disrupting and transforming every industry it touches. From business operations and efficiency to innovative means of customer service, medical research breakthroughs, smarter transport systems and targeted advertising campaigns, it’s an inescapable reality of today’s world. Businesses unwilling to adopt AI will fall behind, as Tractica predicts that the revenue generated from both the direct and indirect application of AI software will grow from $1.38 billion in 2016 to $59.75 billion by 2025. (Tractica, 2017)  

“As machines become smarter, consumers will expect flawless customer service around the clock, and by 2025 AI will drive 95% of all customer interactions, with consumers unable to differentiate bots from human workers via online chats as well as over the phone.”
(Servion, 2017)

Thanks to the availability of huge amounts of data and increasingly intelligent algorithms, machines can learn, speak, make informed decisions and carry out complex tasks in an increasingly effective manner. Not only is this driving research breakthroughs, but implementation in industry is demonstrating the huge potential impact that real-world applications of AI can have on businesses across all industries from retail and advertising, to healthcare, sales and marketing, transport, travel and tourism and so many more.

"It is machine learning, and in particular neural networks, that seem right now to hold huge promise; but the history of artificial intelligence warns us not to assume we can accurately predict what will work, or when."
(Ed Newton-Rex, CEO, Jukedeck)

In the 80s when little progress was being made, three pioneers Yoshua Bengio, Yann LeCun and Geoffrey Hinton toiled away working on neural networks where other scientists had abandoned them due to lack of computational power. ‘In the lean times when no one believed in neural nets, these [were] the people who really kept the torch burning and really inspired a lot of people.’ (Recode, 2015)  

Since then AI has been applied in business,  enhanced through research, and has had some astounding breakthroughs: DeepMind have mastered the Atari console and conquered the game of ‘Go’, and AI is not becoming superior to humans in several areas including object recognition and face detections, as well as working towards the steps of passing the Turing test.  

What actually is AI?

"Depending who you talk to, you usually find two definitions: one where AI aims to embed human intelligence into a machine, and another where AI aims at discovering possibly super-human levels of intelligence. If interpretability of the AI system is important, we might prefer an intelligence that's closer to the human's. But if we wish to design the best AI system that detects diseases in patients, we would be happy if it were better than a human doctor."

(Hugo Larochelle,
Research Scientist, Google Brain)

Who's driving progressions in AI?


It’s not just technology giants leading the AI race, but Universities, venture capitalists and internal researchers. Research from institutions and industry experts opens doors for businesses to apply these models to their work, and AI specific VCs are assisting in breakthroughs through their funding.

What’s allowing us to progress so quickly?

AI requires huge data sets and the coupling of ‘really great science with amazing advances in technology has allowed us to collect data at that we've never had access to before’, enabling models to learn more quickly. (Jasper Snoek, Google Brain) The pace of current advancements wasn’t foreseen, for example Ankur Handa, OpenAI didn’t expect to see ‘super-human performance on ImageNet within only three years of the first paper on CNNs (convolutional neural networks) from Geoffrey Hinton's group in Toronto.’

Whilst these progressions are rapid and impactful, consideration is necessary to identify whether your business in should be applying AI. Factors such as cost, available data, industry relevance and staffing are a consideration for businesses of all sizes as well as the likely ROI. Further chapters will provide solutions to these key points in order to identify the impact of AI in industry and whether you should be employing these technologies in your business.

"Progress has been driven primarily by new ideas and insight rather than bigger datasets and faster computers."
(Jorg Bornschien, DeepMind)

Progress is consistent, yet to have a direct impact on society takes time and money from organisations implementing research progressions. There are limitations in the availability of data, the power of the computation, and the training period and intelligence of each model. Advancements in unsupervised learning are revolutionising business applications saving both time and money - between 2010 and 2014, global investments in AI tech grew from $1.7bn to $14.9bn. Although AI isn’t a new concept, the journey to mainstream implementation has taken years, with recent decades seeing the most rapid advancements. This means that new advances become obsolete quickly thanks to our current culture of using arViv and social media for dissemination. This ‘hyperactivity’ in AI can be disruptive and is forcing companies to reconsider the products they are designing (Hugo Larochelle).

Download the full paper here. Topics explored are:

`interested in contributing to the next edition of the paper? Email Yaz at yhow@re-work.co to suggest the next topic, to sponsor or to get involved! 

Deep Learning Business Case Studies AI Healthcare Business Applications Autonomous Vehicles


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