Artificial intelligence is a young field full of nearly unlimited potential that remains largely misunderstood by most people. We’ve come a long way since Watson won Jeopardy in 2011 and IBM formed the business unit with over $1 billion in investments. AI is no longer a one-trick pony. AI technology from IBM Watson and multiple companies such as WayBlazer and SparkCognition has moved firmly into the real world. It is now being used for a variety of daily applications including:
- Speeding up DNA analysis in cancer patients to help make their treatment more effective
- Ensuring proper advice and experience for customers of wealth and investment management
- Monitoring potential changes to relevant laws and alerting lawyers when changes occur
- Presenting the right products to customers at the most appropriate point in the buying cycle
- Providing trip-planning advice that travelers find relevant
We have no doubt come a good distance on what is indeed a very long road. My colleagues at Intel believe that AI will be bigger than the Internet. Software that can understand context and learn about users as individuals is an entirely new paradigm for computing. But many dangers and problems lie ahead, if we don’t look past the hype and focus on five key areas:
1. Applying AI
It all starts with what you are trying to achieve. Companies are struggling to generate business value with AI. Data scientists are overwhelmed by the complexity and quantity of data, and line-of-business executives for their part are underwhelmed by the tangible output of those data scientists. (See the recently published Harvard Business Review article, “Why You’re Not Getting Value from Your Data Science.”) Machine learning teams are struggling with what business problems to solve with clear outcomes. What is needed is a clear set of high-value use cases by industry and process domains where AI can create demonstrable business value.
2. Building AI.
We have a global talent shortage, and the demand for data scientists continues to grow rapidly, far outpacing the anemic growth in supply. A McKinsey study predicts that by 2018 the number of data science jobs in the United States alone will exceed 490,000, but there will be fewer than 200,000 available data scientists to fill these positions. Globally, demand for data scientists is projected to exceed supply by more than 50 percent by 2018.
In addition, the training offered at universities is too focused on the mathematical and research aspects of AI and machine learning. Largely missing are strategy, design, insights, and change management. This oversight may have serious consequences for graduating students and their future employers–without a multi-disciplinary approach, we will be graduating data scientists capable of designing an algorithm that is mathematically elegant, but doesn’t make strategic sense for the business.
3. Testing AI.
Quality assurance is one of the most important parts of software development. Products must pass a number of tests before they reach the real world—these include unit testing, boundary testing, stress testing, and other practices. In addition, we need systems that deliver the required training data for machine learning of systems. AI is not deterministic—meaning you can receive different results from the same input data when training it. The software learns in different, nuanced ways each time it is trained. So we need new types of software testing that start with an initial “ground truth” and then verify whether the AI system is doing its job.
4. Governing AI.
Every transformative tool that people have created—from the steam engine to the microprocessor—augments human capabilities. Successful use of these tools requires proper governance, and AI is no different; we need governance to ensure that AI is developed the right way and for the right reasons. As the UX designer Mark Rolston wrote last year on Co.Design, “The coming tidal wave of [AI-based decision support software] threatens to give very few people a phenomenal amount of suggestive power over a great many people—the kind of power that is hard to trace and almost impossible to stop.”
AI systems should be manageable and able to clearly explain their actions. Algorithm development has so far been driven by the goal of improving performance, at the expense of credibility and traceability, which means we end up with opaque “black boxes.” We are already seeing such black boxes rejected by users, regulators, and companies, as they fail the regulatory, compliance and risk requirements of corporations dealing with sensitive personal health and financial information. This issue will only get bigger as AI leads to new processes and longer chains of responsibility.
Last year’s White House report on “Preparing for the Future of Artificial Intelligence” outlined key areas of governance:
- Algorithmic Responsibility: Establishing practices and protocols to build understanding and trust in the construction and workings of fundamental algorithms in software code, while preserving proprietary and confidential business information
- Individual Privacy: Establishing strong, sensible protections for individual privacy
- Jobs and Workforce Transformation: New job creation, and workers with skills to fill them
- Safety: Protecting decision-making based on morals and ethics, and establishing controlling principles for autonomous systems
5. Experiencing AI.
One of the biggest stories at the 2017 Consumer Electronics Show in Las Vegas was the exponential growth of Amazon’s Alexa ecosystem. It foretold a future of endless smart home and office products accessible via voice, gesture, and other ways through Amazon Echo. Another tech giant, chipmaker Nvidia, presented an expansive vision for homes, offices, and cars controlled by AI assistants. Meanwhile holographic projection, VR headsets, and “merged” reality technologies like Intel’s Project Alloy showed that the fundamental way we experience computers is evolving.
When it comes to experiencing AI, researchers tend to focus on creating better algorithms. But there’s really much more to be done here. The quality of the user experience determines both the usefulness of the product and its rate of adoption, and this is why I believe design is the next frontier of AI. At the machine intelligence firm CognitiveScale, where I’m chairman, we are facing this challenge with cognitive computing, the type of AI software we create for multinational banks, retailers, healthcare providers, and others. Like a lot of enterprise systems today our software is cloud-based. So how do you make something as nebulous sounding as a “cognitive cloud” something that a user would be thrilled to welcome into her daily life?
“Cognitive design” is the subject of a longer article, but here I will hint that a key strategy is to focus on the micro-interactions between man and machine—the fleeting moments that add up to make engagement with an AI system delightful. Just as designers use tools like journey maps to develop a human-centered experience around a particular product or service, companies must practice “cognitive design thinking”—creating an experience between man and machine that builds efficacy, trust, and an emotional bond. In the end, outcomes are determined as much by the human element as by the software element.
All of this only touches the surface of the issues and difficulties that lie ahead. AI isn’t just software, and it isn’t just about making things easier. Its potential for radical social and economic change is enormous, and it will touch every aspect of our personal and public lives, which is why we need to think carefully and ethically about how we apply, build, test, govern, and experience machine intelligence.