In this episode I talk to Katie King, well known AI author and CEO of AI in Business. I had the pleasure to catch up with her an hour to discuss her journey in the AI industry, tips and perspectives on how to think about AI, common misconceptions, her work with the All-Party Parliament, and the impacts of COVID-19 on the AI industry.
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What is Artificial intelligence
Katie and I discussed the that in the last few years the interest (and hype) in artificial intelligence (AI) has skyrocketed. From big tech firms, startups, venture capital firms, and hobbyists are developing AI technologies that have exceeded $2 billion since 2011.
Artificial intelligence is a much-talked-about emerging technology that will revolutionize entire industries. By using AI, organizations will have the ability to drastically reduce labor costs, generate new and unexpected insights, discover new patterns, and create predictive models from raw data.
As you can see with the Gartner Hype Cycle (the last one I authored) AI and AI based technologies are very hyped right now. I made the prediction a few years ago that by THIS YEAR that:
80% of emerging technologies will have AI foundations.
50% of organizations will lack sufficient AI and data literacy skills to achieve business value
I want to remind people that AI is not a new idea by any stretch. This is actually a 70+ year concept that dates from the 1950s. Throughout history AI has spurred our imagination, but has often left us with wanting much more from it. The graphic below is what I put together a few years ago to articulate the simplified history of AI.
Even today we are still extremely early on the AI journey. The easier part of AI is "Narrow AI", which is dominateed by machine learning. Machine learning is the science of getting computers to act without being explicitly programmed that uses statistical techniques to construct a model.
Again, here is a simplified version of the break down of machine learning. What you will see when you look at this is that AI algorithms are actually embedded in many technologies we use every single day.
General AI has been a general disappointment to date. General AI simply represents the singularity event, or what I referred to as the "Terminator Era". I'm not an expert on General AI but from my research I would say we are at least 30 or 40 years away from anything looking like this happening.
AI impacts to Industries
The "Narrow AI" machine learning technologies are bring transformative capabilities that enable organizations to be successful. The following cross-industry and business scenarios highlight the many possibilities of machine learning.
AI in Hospitality and Restaurants
Sherif Mityas, Chief Experience Officer, TGI Fridays, “With Amperity, Fridays is able to pull in data from point-of-sale systems, social media, credit card transactions and mobile devices. The platform analyses data to create personalised campaigns for the pool of more than 4 million guests who have given Fridays permission to contact them directly.”
A small Japanese restaurant has tripled productivity, reduced food loss by 70%, and increased profits 5x through a developed AI system that predicts (95% accurate) the number of dining customers they’re going to have based on weather and tourism data to predict the number of expected customers up to 45 days in the future.
AI in insurance
Dynamic insurance pricing. Insurance organizations can create prediction models based on specific market conditions such as housing bubbles, historical sales, natural disasters, a surge of burglaries, or opted-in sharing of the consumer's data. These models can dynamically adjust insurance rates.
Making insurance payout decisions. Fukoku Mutual Life Insurance replaced 34 employees and saves an estimated $1.21 million each year by using AI. The company uses AI to read tens of thousands of medical certificates and factor in the lengths of hospital stays, medical histories and any surgical procedures before calculating payouts.
Fraud detection and prevention. With an estimated 80 billion fraudulent claims reported in the U.S. each year, insurance companies have the opportunity to use AI to autovalidate policies to ensure that the information provided to support the claim is correct and justifies payment. If validated, the transaction is automatically sent to the downstream processes with payouts done in near real time without any human involvement.
Ageas customers in the UK can submit photographs as they report accidents, via smartphones and can get decisions on their next steps within minutes and in some cases while they are on their initial phone call to the insurer.
Banking
Optimized lending. Machine-learning solutions can map a loan applicant's details (such as demographics, as well as credit and payment history) to predict the likelihood that the applicant will default on a loan
Retail banking fraud detection. Algorithms can be created to assess and model current real-time transactions, as well as build predictive models of transactions based on their likelihood of being fraudulent.
Crowdfunding, peer-to-peer lending and direct lending. AI can be used to spur new customer engagement modes, increase the speed to value and provide an unrivaled customer experience.
Healthcare
Medical diagnostics. Machine learning can provide doctors with a more accurate classification of a patient's medical condition, including recommendations for therapy or treatment. It does this by assembling data such as current vital signs, symptoms or historical vital signs from sources including home lab tests and algorithmic medical devices (for example, Eko Core).
Calming patients using AI. By applying algorithms to identify patterns of parent fears as primary drivers for avoiding vaccinations, healthcare company GlaxoSmithKline used Luminoso's natural-language and text analytics technology as a noninvasive solution for gaining insight into parents' growing concerns about vaccinations.
Computer-assisted diagnosis (CAD). Companies like Medecision have developed algorithms to identify variables used to predict avoidable hospitalizations in diabetes patients. Other AI-fueled CAD solutions have been estimated to have spotted 52% of breast cancers in women as much as a year before they were officially diagnosed.
Creation of safe working conditions. Organizations with workers in potentially unsafe environments can use machine learning to detect early warning signs that may predict the likelihood of accidents. In this use, machine learning examines sensor data from the measurement of air quality, equipment performance, employee productivity and even atypical behavior.
Public Sector
Real-time decisioning by city utilities. Machine learning can create probabilistic models from wind turbines, solar panels and soil actuators, for example, to predict when failures will occur. This enables utilities to dynamically redirect power or water, decrease maintenance costs and minimize downtime.
Education
Enhancement of the student experience. Deakin University in Australia is using IBM Watson to help students find information easily
Artificial Intelligence Responding to COVID-19
There is a significant amount of leadership and momentum around the usage of AI to help solve the global pandemic.
A few examples include:
The Allen Institute is a great example of this. They have exposed CORD-19 open research dataset of more than 128,000 research papers and other materials, this machine learning solution can extract relevant medical information from unstructured text and delivers robust natural-language query capabilities, helping to accelerate the pace of discovery.
Another example, Microsoft has launched AI for Health, that is focusing on helping those on the front lines of research of COVID-19. Microsoft is immediately dedicating $20 million to this specific effort and will focus on five areas where data, analysis, and the skills of our data scientists can have the biggest impact:
Data and insights to inform for people’s safety and economic impacts
Treatment and diagnostics, enabling research to further the development of vaccines, diagnostics, and therapeutics
Allocation of resources, including recommendations on the allocation of limited assets, such as hospital space and medical supplies
Dissemination of accurate information to minimize misinformation sharing
Scientific research to study and understand COVID-19.
Even the White House is getting in on the AI action. They announced the launch of the COVID-19 High Performance Computing Consortium, powered by AI, to provide COVID-19 researchers worldwide with access to the world’s most powerful high performance computing resources that can significantly advance the pace of scientific discovery in the fight to stop the virus.
How Katie King can Help You
Katie walked us through If you are looking for guidance in adopting new technologies such as AI or machine learning into your strategy and operations, as well as her specialised consulting through our subsidiary company, AI in Business. Check out her website aiinbusiness.co.uk for more information.
Lots more in this episode to check out. Remember to leave a comment on your thoughts!
About Katie King
Katie is a member of the UK Government All-Party Parliamentary Group (APPG) task force for the enterprise adoption of AI.
Katie is an Associate Director at the Rialto Consultancy, an award-winning consultancy that drives leadership and business success.
Katie works closely with the private sector, and collaborates with schools, colleges and universities, focusing on many of the big issues surrounding skills, employment, community and ethics. Katie works closely with School Speakers, the Association of Colleges (AoC), as well as colleges and universities. This is in addition to her extensive work in industry, spanning consultancy, training and speaker engagements in sectors as diverse as tourism, digital, facilities management and professional services.
Katie has over 30 years’ experience and has advised many of the world's leading brands and business leaders, including Richard Branson/Virgin, o2, Orange, Accenture, PA Consulting, Huawei, Arsenal Football Club and Harrods.