Today’s artificial intelligence (AI) revolution has been made possible by the algorithm revolution. The machine learning algorithms researchers have been developing for decades, when cleverly applied to today’s web-scale data sets, can yield surprisingly good forms of intelligence. For instance, the United States Postal Service has long used neural network models to automatically read handwritten zip code digits. Today’s deep learning neural networks can be trained on millions of electronic photographs to identify faces, and similar algorithms may increasingly be used to navigate automobiles and identify tumors in X-rays. The IBM Watson information retrieval system could triumph on the game show “Jeopardy!” partly because most human knowledge is now stored electronically.

But current AI technologies are a collection of big data-driven point solutions, and algorithms are reliable only to the extent that the data used to train them is complete and appropriate. One-off or unforeseen events that humans can navigate using common sense can lead algorithms to yield nonsensical outputs.

Design thinking is defined as human-centric design that builds upon the deep understanding of our users (e.g., their tendencies, propensities, inclinations, behaviours) to generate ideas, build prototypes, share what you’ve made, embrace the art of failure (i.e., fail fast but learn faster) and eventually put your innovative solution out into the world.  And fortunately for us humans (who really excel at human-centric things), there is a tight correlation between the design thinking and artificial intelligence.

Artificial intelligence technologies could reshape economies and societies, but more powerful algorithms do not automatically yield improved business or societal outcomes. Human-centered design thinking can help organizations get the most out of cognitive technologies.

Divergence from More Powerful Intelligence To More Creative Intelligence

While algorithms can automate many routine tasks, the narrow nature of data-driven AI implies that many other tasks will require human involvement. In such cases, algorithms should be viewed as cognitive tools capable of augmenting human capabilities and integrated into systems designed to go with the grain of human—and organizational—psychology. We don’t want to ascribe to AI algorithms more intelligence than is really there. They may be smarter than humans at certain tasks, but more generally we need to make sure algorithms are designed to help us, not do an end run around our common sense.

Design Thinking at Enterprise Premise

Although cognitive design thinking is in its early stages in many enterprises, the implications are evident. Eschewing versus embracing design thinking can mean the difference between failure and success. For example, a legacy company that believes photography hinges on printing photographs could falter compared to an internet startup that realizes many customers would prefer to share images online without making prints, and embraces technology that learns faces and automatically generates albums to enhance their experience.

To make design thinking meaningful for consumers, companies can benefit from carefully selecting use cases and the information they feed into AI technologies. In determining which available data is likely to generate desired results, enterprises can start by focusing on their individual problems and business cases, create cognitive centres of excellence, adopt common platforms to digest and analyze data, enforce strong data governance practices, and crowdsource ideas from employees and customers alike.

In assessing what constitutes proper algorithmic design, organizations may confront ethical quandaries that expose them to potential risk. Unintended algorithmic bias can lead to exclusionary and even discriminatory practices. For example, facial recognition software trained on insufficiently diverse data sets may be largely incapable of recognizing individuals with different skin tones. This could cause problems in predictive policing, and even lead to misidentification of crime suspects. If the training data sets aren’t really that diverse, any face that deviates too much from the established norm will be harder to detect. Accordingly, across many fields, we can start thinking about how we create more inclusive code and employ inclusive coding practices.

CXO Strategy for Cognitive Design Thinking

CIOs can introduce cognitive design thinking to their organizations by first determining how it can address problems that conventional technologies alone cannot solve. The technology works with the right use cases, data, and people, but demonstrating value is not always simple. However, once CIOs have proof points that show the value of cognitive design thinking, they can scale them up over time.

CIOs benefit from working with business stakeholders to identify sources of value. It is also important to involve end users in the design and conception of algorithms used to automate or augment cognitive tasks. Make sure people understand the premise of the model so they can pragmatically balance algorithm results with other information.

Enterprise Behavioral Science – From Insights to Influencing Business Decisions

Every January, how many people do you know say that they want to resolve to save more, spend less, eat better, or exercise more? These admirable goals are often proclaimed with the best of intentions, but are rarely achieved. If people were purely logical, we would all be the healthiest versions of ourselves.

However, the truth is that humans are not 100% rational; we are emotional creatures that are not always predictable. Behavioral economics evolved from this recognition of human irrationality. Behavioral economics is a method of economic analysis that applies psychological insights into human behavior to explain economic decision-making.

Decision making is one of the central activities of business – hundreds of billions of decisions are made everyday. Decision making sits at the heart of innovation, growth, and profitability, and is foundational to competitiveness. Despite this degree of importance, decision making is poorly understood, and badly supported by tools. A study by Bain & Company found that decision effectiveness is 95% correlated with companies’ financial performance.

Enterprise Behavioral Science is not only about understanding potential outcomes, but to completely change outcomes, and more specifically, change the way in which people behave. Behavioral Science tells us that to make a fundamental change in behavior that will affect the long-term outcome of a process, we must insert an inflection point.

As an example, you are a sales rep and two years ago your revenue was $1 million. Last year it was $1.1 million, and this year you expect $1.2 million in sales. The trend is clear, and your growth has been linear and predictable. However, there is a change in company leadership and your management has increased your quota to $2 million for next year. What is going to motivate you to almost double your revenues? The difference between expectations ($2 million) and reality ($1.2 million) is often referred to as the “behavioral gap” . When the behavioral gap is significant, an inflection point is needed to close that gap. The right incentive can initiate an inflection point and influence a change in behavior. Perhaps that incentive is an added bonus, President’s Club eligibility, a promotion, etc.

Cognitive Design Thinking – The New Indispensable Reskilling Avenue

Artificial intelligence, machine learning, big data analytics and mobile and software development will be the top technology areas where the need for re-skilling will be the highest. India will need 700 million skilled workforce by 2022 to meet the demands of a growing economy. Hence, while there is a high probability that machine learning and artificial intelligence will play an important role in whatever job you hold in the future, there is one way to “future-proof” your career…embrace the power of design thinking.

In fact, integrating design thinking and artificial intelligence can give you “super powers” that future-proof whatever career you decide to pursue. To meld these two disciplines together, one must:

• Understand where and how artificial intelligence and behavioural science can impact your business initiatives. While you won’t need to write machine learning algorithms, business leaders do need to learn how to “Think like a data scientist” in order understand how AI can optimize key operational processes, reduce security and regulatory risks, uncover new monetization opportunities.
• Understand how design thinking techniques, concepts and tools can create a more compelling and emphatic user experience with a “delightful” user engagement through superior insights into your customers’ usage objectives, operating environment and impediments to success.

Design thinking is a mindset. IT firms are trying to move up the curve. Higher-end services that companies can charge more is to provide value and for that you need to know that end-customers needs. For example, to provide value services to banking customers is to find out what the bank’s customer needs are in that country the banking client is based. Latent needs come from a design thinking philosophy where you observe customer data, patterns and provide a solution that the customer does not know. Therefore, Companies will hire design thinkers as they can predict what the consumer does not know and hence charge for the product/service from their clients. Idea in design thinking is to provide agile product creation or solutions.

Without Design Thinking & Behavioural Science, AI Will be Only an Incremental Value

Though organizations understand the opportunity that big data presents, many struggles to find a way to unlock its value and use it in tandem with design thinking – making “big data a colossal waste of time & money.” Only by combining quantitative insights gathered using AI, machine/deep learning, and qualitative research through behavioural science, and finally design thinking to uncover hidden patterns and leveraging it to understand what the customer would want, will we be able to paint a complete picture of the problem at hand, and help drive towards a solution that would create value for all stakeholders.

At the turn of the century, it’s likely few, if any, could anticipate the many ways artificial intelligence would later affect our lives.

Take Emotional Robot with Intelligent Network, or ERWIN, for example. He’s designed to mimic human emotions like sadness and happiness in order to help researchers understand how empathy affects human-robot connections. When ERWIN works with Keepon—a robot who looks eerily similar to a real person—scientists gather data on how emotional responses and body language can foster meaningful relationships in an inevitably droid-filled society. Increasingly, robots are integrating into our lives as laborers, therapeutic and medical tools, assistants and more.

While some predict mass unemployment or all-out war between humans and artificial intelligence, others foresee a less bleak future.

The Machine-Man Coexistence

Professor Manuela Veloso, head of the machine learning department at Carnegie Mellon University, is already testing out the idea on the CMU campus, building roving, segway-shaped robots called “cobots” to autonomously escort guests from building to building and ask for human help when they fall short. It’s a new way to think about artificial intelligence, and one that could have profound consequences in the next five years.

There will be a co-existence between humans and artificial intelligence systems that will be hopefully of service to humanity. These AI systems will involve software systems that handle the digital world, and also systems that move around in physical space, like drones, and robots, and autonomous cars, and also systems that process the physical space, like the Internet of Things.

You will have more intelligent systems in the physical world, too — not just on your cell phone or computer, but physically present around us, processing and sensing information about the physical world and helping us with decisions that include knowing a lot about features of the physical world. As time goes by, we’ll also see these AI systems having an impact on broader problems in society: managing traffic in a big city, for instance; making complex predictions about the climate; supporting humans in the big decisions they have to make.

Digital – The Ultimate Catalyst to Accelerate AI

A lot of [AI] research in the early days was actually acquiring [that] knowledge. We would have to ask humans. We would have to go to books and manually enter that information into the computer.

 in the last few years, more and more of this information is digital. It seems that the world reveals itself on the internet. So AI systems are now about the data that’s available and the ability to process that data and make sense of it, and we’re still figuring out the best ways to do that. On the other hand, we are very optimistic because we know that the data is there.

The question now becomes, how do we learn from it? How do you use it? How do you represent it? How do you study the distributions — the statistics of the data? How do you put all these pieces together? That’s how you get deep learning and deep reinforcement learning and systems that do automatic translation and robots that play soccer. All these things are possible because we can process all this data so much more effectively and we don’t have to take the enormous step of acquiring that knowledge and representing it. It’s there.

Rules of Coexistence

As of late, discussions have run rampant about the impact of intelligent systems on the nature of work, jobs and the economy. Whether it is self-driving cars, automated warehouses, intelligent advisory systems, or interactive systems supported by deep learning, these technologies are rumored to first take our jobs and eventually run the world.

There are many points of view with regard to this issue, all aimed at defining our role in a world of highly intelligent machines but also aggressively denying the truth of the world to come. Below are the critical arguments of how we’ll coexist with machines in the future:

Machines Take Our Jobs, New Jobs Are Created

 Some arguments are driven by the historical observation that every new piece of technology has both destroyed and created jobs. The cotton gin automated the cleaning of cotton. This meant that people no longer had to do the work because a machine enabled the massive growth of cotton production, which shifted the work to cotton picking. For nearly every piece of technology, from the steam engine to the word processor, the argument is that as some jobs were destroyed, others were created.

Machines Only Take Some Of Our Jobs

A variant of the first argument is that even if new jobs are not created, people will shift their focus to those aspects of work that intelligent systems are not equipped to handle. This includes areas requiring the creativity, insight and personal communication that are hallmarks of human abilities, and ones that machines simply do not possess. The driving logic is that there are certain human skills that a machine will never be able to master.

A similar, but more nuanced argument portrays a vision of man-machine partnerships in which the analytical power of a machine augments the more intuitive and emotional skills of the human. Or, depending on how much you value one over the other, human intuition will augment a machine’s cold calculations.

Machines Take Our Jobs, We Design New Machines

Finally, there is the view that as intelligent machines do more and more of the work, we will need more and more people to develop the next generation of those machines. Supported by historical parallels (i.e. cars created the need for mechanics and automobile designers), the argument is that we will always need someone working on the next generation of technology. This is a particularly presumptuous position as it is essentially technologists arguing that while machines will do many things, they will never be able to do what technologists do.

But Could Coexistence Exist Eternally?

These are all reasonable arguments above, and each one has its merits. But they are all based on the same assumption: Machines will never be able to do everything that people can do, because there will always be gaps in a machine’s ability to reason, be creative or intuitive. Machines will never have empathy or emotion, nor have the ability to make decisions or be consciously aware of themselves in a way that could drive introspection.

These assumptions have existed since the earliest days of A.I. They tend to go unquestioned simply because we prefer to live in a world in which machines cannot be our equals, and we maintain control over those aspects of cognition that, to this point at least, make us unique.

But the reality is that from consciousness to intuition to emotion, there is no reason to believe that any one of them will hold. It is quite conclusive that the only alternative to the belief that human thought can be modeled on a machine is to believe that our minds are the product of “magic.” Either we are part of the world of causation or we are not. If we are, A.I. is possible.