Introduction: Moving From Experimental to Mainstream
As we heard at our end-of-year community drinks event, it is clear that AI is now crossing the chasm faster than any technology before it. We are now moving from experimental to mainstream. This is an exciting and important time to deploy in your organisation. 2018 will be all about turning these 2017 experiments into 2018 realities. We thought it might be useful for you to provide you with a roundup of what the major publications, thought leaders, and enterprises think is in store for the future of AI in 2017.
Future of Work Predictions
Some of most frequently cited statistics surrounding AI and automation are focused on the number of displacement of jobs. However, the predictions we have seen are not all doom and gloom.
Upskilling: as displacement becomes a reality, a critical question which arises is: how is society to help and support those who have been displaced?
- Sophie Christie (Telegraph): More companies will focus on ‘upskilling’ employees, as AI and robotics advance in the workplace
Changing the Nature of Jobs by Augmenting Humans
- Herman Man (VP of product and partnerships, Xero): AI will fundamentally alter the role of today’s accountant as focus shifts from tedious data entry, to using data-driven insights uncovered by machine learning to help small business clients make better business decision
- Justin Fier (Director for Cyber Intelligence and Analysis, Darktrace): in cybersecurity, AI will hel humans allocate resources effectively and spend their time on the most important priorities
In 2017, we saw self-driving technology move forward at a steady pace. The development of the technology has matured quickly, but we are still in the early days in terms of building out the self-driving experience and cybersecurity.
- Dr. Rana el Kaliouby (CEO and co-founder, Affectiva): we will see the rise of an AI-based computer vision solution to ensure safe driving, seamless handoffs to a human driver, and an enriched travel experience based on the emotional, cognitive and wellness of the occupants
Safety: This year we saw a deadly car crash in a Tesla, which got us thinking about seriously put the notion of safety of autonomous vehicles on the map.
- Monty Barlow (director of machine learning, Cambridge Consultants): in 2018, we’ll have the realisation that human-level autonomous driving will require much longer to test and mature than current optimistic predictions
Road-Ready: As of July 2017, 36 businesses authorised to test self-driving cars in California (up over 3x from 2016); what will the road of 2018 look like?
- Azeem Azhar (Exponential View): Autonomous vehicles will broadly remain in testing, but we’ll see many more pilot programmes.
Transparency and Ethics Predictions
A perennial problem in AI is the so-called ‘Black Box Problem’, or the idea that we can’t quite get to the bottom of the reason behind algorithmic decision making. In 2018, some predict that we will demand more transparency and will see it come to fruition.
- Or Shani, (CEO, Albert AI): developers will begin prioritizing advanced forms of accountability, reporting and system queries that allow users to ask, “Why?”, in response to very specific actions
- Aaron Kalb (co-founder and head of product, Alation): high quality training data will become a coveted resource
- Michel Morvan (co-founder and CEO, Cosmo Tech USA): people will become disillusioned as the experts acknowledge that there is only so much that AI will be able to do
- Anand Rao, Joseph Voyles and Pia Ramchandani (PWC): Understanding precisely how deep learning works enables its greater development and use; we will also push for more explainable AI in general
- Bradley J. Erickson ,MD, PhD (consultant for Department of Radiology): There will be much fewer concerns about deep learning being a ‘black box,’ as new techniques will help us understand what DL is ‘seeing‘.
- George Shih (founder, MD.ai): We will start seeing more and more people from all kinds of backgrounds participating in building, developing and productizing AI
Retail, Marketing, and Personalisation Predictions
In retail: personalisation is king. According to a McKinsey report, 61% of consumers are more likely to buy from companies that deliver custom content based on real-time interactions.
- Dan Baruchi (CEO, Personali): the rise of personalised dynamic pricing
- Pehr Luedtke (SVP of Business Development, Valassis Digital): AI will continue to grow as advertisers gain a better understanding of how the technology can fit into their customer engagement plans and enhance the shopping experience
- Dan Rosenberg (Chief Strategy Officer, MediaMath): we’ll finally start to see AI deliver on the omnichannel promise to make marketing that consumers—and others in the value chain—love
- Bryan Chagoly (VP of Technology, Bazaarvoice): AI will help marketers correlate and synthesise signals from different sources more efficiently than before
- Forrester: Intelligent agents will directly influence 10% of purchase decisions, 67% of retailers will be unprepared to exploit intelligent agents, and 25% of brands will lack expertise in the lingua franca of intelligent agents
- Jan Kautz (senior director of Visual Computing and Machine Learning Research, NVIDIA): AI to be able to create new personalized media, such as music according to your taste
Investing in AI Predictions
Investment in AI is red hot to the extent that 80% of enterprises are investing in AI today (Vanson Bourne study).
- Subrata Chakrabarti (VP of Product Marketing and Strategy, Anaplan): continued investments in AI by VCs and from technology and non-technology sectors
- Nicola Morini Bianzino (MD of Artificial Intelligence and growth & strategy lead of Technology, Accenture): AI is going to affect 25 percent of technology spend going forward
Business Deployment Predictions
According to recent survey findings from Gartner reveal that almost 60% of organisations surveyed have yet to take advantage of the benefits of AI. 2018 will be the year when more and more enterprises will take the plunge and go from Lab to Live.
- Guy Levy-Yurista (Head of Product, Sisense): AI will finally be able to ‘understand’ business data in lieu of simply reporting on it
- Timo Elliott (Innovation Evangelist, SAP): all products, services, and business processes will be self-improving
- Stanton Jones (Director and Principal Analyst, ISG): narrow AI applications will make a big splash in enterprise support functions in 2018
- Tomer Naveh (CTO, Albert): AI interfaces will become so accessible that non-technical users across organizations and roles will be able to operate them
- Derek Choy (CIO, Rainforest QA): Companies will turn to AI to help scale and do jobs instead of adding headcount
- Forrester: 75% of early AI projects will underwhelm due to operational oversights
Rate of Deployment
- Markus Noga (Head of Machine Learning, SAP): More and more companies will grow out of proof of concepts and will effectively start to apply AI throughout the business
- Nima Negahban (CTO and cofounder, Kinetica): investments in AI life cycle management will increase and technologies that house the data and supervise the process will mature
- Deloitte: predict that machine learning will intensify in 2018 among large and medium-sized enterprises in Canada, with double the number of implementations and pilot projects using machine-learning technology compared to 2017
According to CBInsights, Health and wellness is the hottest area of investment in AI, with over 270 deals going to the category since 2012. This year, we saw Berlin’s Ada Health raises $47M to become the Alexa of healthcare and a healthcare startup has raised $30M for its ‘Sophia’ AI.
Projections for 2018 say that AI and robotics will be ubiquitous in the coming year (IDC predicts that 50% of surgeons will use computer assisted or robotic surgery techniques) and we have seen the blossoming of this fruit in 2017.
- Christian Boucher (Healthcare Evangelist, Citrix): AI will play a major role in healthcare in optimising workflows both within in-patient and out-patient scenarios
- Frank Ingari (CEO, Growth Ally): 2018 will be the year that exposes where AI works and where it fails in healthcare
- Mark Michalski (executive director, Massachusetts General Hospital and Brigham and Women’s Center for Clinical Data Science): We’re going to move from algorithms to products and think more about integration and validation
- Safwan Halabi (medical director of Radiology Informatics, Stanford Children’s Health, Lucile Packard Children’s Hospital): we will begin to see AI-enabled tools translate from the research lab to the radiologist workstation and ultimately the patient bedside
Bot and Personal Assistants Predictions
- Dharmesh Shah (co-founder and CTO, HubSpot): we’ll see bots start making it possible for businesses to provide answers to the most common questions their customers have
- Farzein Shahidi (Nextplane and Intrprtr): enterprises will adopt strategic and unique ways to weave AI into their day-to-day interactions and automate conversations for maximum efficiency
- Alejandro Troccoli (senior research scientist, NVIDIA): Personal assistant AIs will keep getting smarter, being able to prepare your dinner for you without asking
- Georges Nahon, CEO, Orange Silicon Valley: the face will be the new credit card, the new driver’s license, and the new barcode
- Robinson Piramuthu (chief scientist for computer vision, eBay): Vast applications on smartphones will run deep neural networks to enable AI
- Chris Nicholson (CEO and co-founder, Skymind.io): Robots are going to get better at complex tasks that humans still take for granted, like walking around a room and over objects
- Azeem Azhar (Expontential View): AI development will accelerate up-and-down the stack and we’ll be blown away by the performance of custom ASICs, novel experiments in non-deep learning areas
- DigitalOcean: 73% of developers surveyed plan to learn about AI and machine learning in 2018
Azeem’s Azhar: Crowdsourced Predictions on Tech & Society for 2018
VentureBeat: 2018 is the year chatbots join the enterprise
Forrester: Predictions 2018: A year of reckoning
MarTech Today: The CMO’s guide to AI’s marketing impact for 2018
NVIDIA: Where Is AI Headed in 2018?
Deloitte: TMT Predictions 2018
Kantar & Millward Brown: Media & Digital Predictions 2018
VentureBeat: An investor’s view of AI in 2018
Nieman Lab: Predictions for Journalism 2018Published in