Riding the Exponential Curve in AI's Tail

The majority of us today have AI in our pockets, in the form of Siri, Cortana or Bixby. While these technologies might seem extremely sophisticated, they are still, in fact, considered to be “narrow” or “weak” AI. But imagine a world in which machines could interact and think, not just in terms of data collection, but in a reasoned and (almost) sentient manner. AI in this context would radically change our daily lives (think Ex Machina or I, Robot). But is there any fact to the fiction portrayed in these movies?

For AI to operate in this manner, the technology would have to evolve to the next phase of AI, namely artificial general intelligence (AGI). At this stage, AI reaches the same general intelligence as a human. But how close are we to this milestone? The consensus, among futurists and analysts, is that AGI will only begin to emerge in roughly the next 20 years.

That 20-year estimate, however, is starting to look a little shaky, especially as advancements in narrow AI are coming thick and fast and have changed the way AI operates. Our job at the CFOW is to monitor these technologies and their impact on work. Let’s examine some platforms at the leading edge of this development.

  • Google’s AlphaGo. Yes, we’ve all heard of AlphaGo beating professional Go player Lee Sedol. But do we understand the implications of this victory? The AI used in this instance is vastly superior to any AI used before that has beaten humans at strategic games. When IBM’s Deep Blue beat chess grandmaster Garry Kasparov in 1997, the computer calculated all possible moves and then made an optimal decision based on the outcome of these moves.

    The game Go has many more possible moves than chess – as many more as there are atoms in the universe (yes, really) – so calculating all potential moves is impossible given modern computing power. Instead, AlphaGo used deep reinforcement learning and neural networks to mimic the learning and reasoning capabilities of the human brain. What makes all this so interesting and potentially revolutionary is that AlphaGo is general-purpose AI and is not preprogrammed but instead uses raw pixel input. Therefore, this type of AI could potentially be the predecessor to AGI.
  • StackGAN. This is one you might not have heard of (but you will soon). StackGAN is a text-to-photo synthesizer. You enter text such as “This flower has overlapping pink pointed petals surrounding a ring of short yellow filaments,” and the AI synthesizes a photo-quality image. On the face of it, this sounds exactly like a Google search, but here the AI completely synthesizes that image from scratch.

    But what really makes this interesting is its use of dual neural networks. Here, one neural network produces an image, and the next decides whether the image is real or fake and then fixes any defects. This type of network is referred to as a generative adversarial network, a form of unsupervised machine learning.
  • Gamalon. This company is creating AI that can write its own code based on experience and probability instead of hard variables, essentially automating much of the human input of program coding and training of AI. This means that Gamalon can learn significantly more quickly than other AI platforms. Gamalon uses “Bayesian Program Synthesis” – writing its own code that best explains the data in front of it. The system is taught to recognize a cat with only a few examples – whiskers, tail, eyes. From there, it can continually update its understanding of what a cat looks with each new example. This is the opposite of what deep learning offers, where you have to train the system by showing it as many examples of a cat as you can. Deep learning can't handle uncertainty.

Fast-Moving Developments

So why are these three technologies important? Well, we are continuously told that AI is still firmly in the “narrow” camp – think Amazon’s Alexa, which is essentially a slick real-speech interface with an Internet search capability. But the reality is that AI is progressing significantly faster than we anticipated. AlphaGO, for example, was 10 years ahead of expected AI capabilities at the time of its Go victory .

Also, these advances, as demonstrated by StackGAN and Gamalon, open the door for significantly more intelligent machines that can teach and program themselves. It also helps that widely available and cheap – but incredibly fast – CPUs are now available, as well as access to GPUs (graphics processing untis), coupled with open platform applications, such as Caffe, CNTK and Mahout. These are opening up AI development to the general public, meaning we now have an exponentially greater number of developers worldwide, further fueling development.

Incremental Advancements Add Up

Making predictions about the pace of technology is a risky business; what’s clear, however, is that we are fast approaching a point where the outputs from machine learning are set to improve exponentially as generative adversarial networks and self-programming systems advance. The three platforms listed here aren’t going to single-handedly bring about AGI. But the convergence of these technologies could usher in a wave of self-coding and self-creating artificial general intelligence.

So to answer my question of whether there is any truth to those fictional AI films, the answer is a resounding maybe! My apologies that it’s not more clear cut yet, but the simple fact is that even though we are so close to technology potentially having this capability, understanding the real-world application is incredibly difficult to do. Imagine Adam Smith foreseeing the Internet ... while the gap between his death and the birth of the Web was 201 years, not 20, the technological pace of change compresses timeframes immensely. It’s not far-fetched to believe that in human years, anyway, the gap between now and the age of AGI is just as wide.


Five Essential Things That "The Work AHEAD" Teaches Us About Digital In The Middle East

Earlier this month, Cognizant hosted “The Work AHEAD” CXO roundtable in Dubai. The venue was Burj Al Arab; with its groundbreaking architecture and breathtaking view from the 27th floor, there could be no better place than to discuss the future of work. Around 25 C-level executives attended the event. I kicked off the session by sharing key insights from our “The Work AHEAD” paper and our latest book What to Do When Machines Do Everything. While every company will follow a different path to its future, a few nuts-and-bolts lessons can be learned from those who have perfected digitalization. We had some eye-opening conversations, and below I would like to share the key takeaways from our talks:

The Future is Hardly in the Future, It’s Already Here. Artificial Intelligence (AI) is the great story of our time. AI, which was confined to the laboratory for decades, is now set to make an appearance on the main stage. Dubai is already on the way to becoming an AI-driven nation. Smart Dubai Government Establishment, the technology arm of Smart Dubai Office, recently outlined a development roadmap for AI in Dubai. As part of this plan, it has set up an AI lab to provide training and go-to-market support for new services, and to offer workshops for experimenting and building prototypes. Meanwhile, the first robot cop is all set to join Dubai’s police force in May ’17. The Dubai Police envisions that these new recruits will make up about 25% of its workforce by 2030. Altogether, it feels like science fiction is already a reality in the city.

In short, the future of work is the mirror image of the future of AI. Our research shows that of the Middle East executives we surveyed, 98% said the rise of AI would have a moderate or strong impact on their work by 2020. If you’re not making AI your business today, it may well put you out of business tomorrow.

Hybrid is the New Normal. While all our participants agreed that digital is the future of their business, they also felt that they were not destined to become pure digital-native companies. Instead, it is all about striking the right balance between the physical and virtual worlds. However, there is a catch. Right now, Middle East companies are generating 2.5% of their revenue from various digital channels, a figure that is expected to increase to 6% by the end of 2018. Compare this with the global average of 11.4% and you will see why our participants feel that they are not getting as much as they should be out of digital. However, they will need to get prepared for the rapid changes to come and move away from the more sedate pace of business that was the norm in the past. Shifts in the market that used to take place over decades now occur in a matter of weeks and months.

How Much Digital is Too Much Digital for a Customer? One of the CXOs shared a very interesting perspective. His company had invested in building a digital offering, assuming that it would make their customers’ lives easier and help them improve customer acquisition and retention. It turned out that their clientele were not ready for such a novelty and the initiative failed to take off. What can be taken from this story is the need for organizations to adopt an “outside-in” rather than an “inside-out” approach. Instead of making your own assumptions, ask the customers what changes they would want to see in your firm. It’s all about mapping the customer’s journey, rather than adopting a piecemeal approach to understanding their experience. You must learn about digital by observing your customers, instead of relying on what you already know about them. This means recognizing that every digital moment is a moment of truth. It means building digital experiences that are engaging, long-term, frictionless, invisible, and, with luck, noteworthy, all executed on a massive scale.

From Analog to Digital Leadership – A Changing Paradigm for Changing Times. The most significant issue here is organizational complacency and resistance to change; firms have to recognize that they must adapt or die. Senior leadership must stop asking their teams to just “fix the problem” and instead ask them to “fix their digital quotient.” Digital transformation requires board-level support and effective communication, and that is where the problem lies. One of the CIOs of a large insurance company mentioned that persuading the board to invest in digital is one of the key challenges he is facing. Forrester’s CEO, George Colony, once rightly said: “If the board has a low digital IQ, the company will have a low digital IQ.”

This is where we hope our “The Work AHEAD” findings can be most useful. As a leader, you need to make the argument better, more complete, and more convincing for the board. A great way to start is knowing how your organization is performing against others in your industry. Get a rough idea of whether you are paying the Laggard Penalty (the difference between cost and revenue performance due to technology) to help you establish the much-needed financial justification for taking the first bold steps toward becoming digital.

The Battle for Next-Generation Talent is Heating Up. My colleague, Euan Davis, also presented at the event, speaking about the digital workforce of the future. A fusion of new algorithms, automation, machine learning, and digital platforms is radically changing what human talent looks like, where to find it, and how it is put to work. Many companies are starting to jettison old and rigid organizational models and building smaller, nimbler clusters of talent that serve a particular market or niche. He talked about how talent clusters are emerging everywhere and why it is important for Middle East companies to take full advantage of this.

It’s interesting to see how firms are starting to build proprietary platforms and driving third parties to engage in co-innovation initiatives around R&D or customer engagement. Clearly, executives need to unlock experimental business processes and harness the opportunities these emerging clusters and platforms provide.

Surprisingly, we did not sense a great deal of concern among the attendees over the job automation that is coming with the emergence of new machines. In fact, they displayed confidence that AI, algorithms, and big data would enhance their current workforce.

We live in exciting times, as digital is moving from toys to tools and becoming the work that matters. You and your teams need to ask, “Do we have the right business model and the right technology model to compete in our industry going forward?” Don’t get overwhelmed by your current set of challenges. Be optimistic; pessimism has never been a great business model. Of course, setbacks will always occur, but shrinking from the future — just as it’s about to get really interesting — will place serious restrictions on your career.

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The Coming Jobs Boom in the Age of Intelligent Machines

Central to the election of President Trump, the populist support of Brexit, and the distrust of so many institutions at the heart of the modern world, are concerns about the future of work; Who or what has taken my job? what are the jobs of the future? How do I – or my kids – get them? Where are the growth opportunities?

We are all sense that the world is rapidly changing and many long-established industries are struggling to stay abreast of this change. We are inundated by stories of jobs and skills that used to generate a good, solid middle class life becoming less valuable than they used to be. And now, many people are increasingly conscious that work is about to change again as machines and software get smarter and smarter and do the work that people used to do. Even highly remunerated financial analysts are increasingly being replaced by software.

All of this has led to the widespread sense of angst and gloom. To many, the future seems a scary place. In reality though, the current zeitgeist portends a new – and we believe better - economy which is rapidly emerging; one based on new intelligent machines. The LSE’s Carlota Perez has written extensively about what happens between the end of one era and the beginning of the next;

History can teach us a lot. Innovation has indeed always been the driver of growth and the main source of increasing productivity and wealth. But every technological revolution has brought two types of prosperity. The first type is turbulent and exciting like the bubbles of the 1990s and 2000s and like the Roaring Twenties, the railway mania, and the canal mania before. They all ended in a bubble collapse. Yet, after the recession, there came the second type: the Victorian boom, the Belle Époque, the Post War Golden Age and . . . the one that we could have ahead now. Bubble prosperities polarize incomes; Golden Ages tend to reverse the process.

Our research and analysis clearly points to one conclusion; that we stand on the cusp of an explosion of economic growth, the like of which will make previous periods of high growth look anemic. We are truly at a moment where we can say that the darkest moment is just before dawn.

Three forces are currently at work that will spark a boom economy and generate explosive job growth:

1. Building Digital Infrastructure Creates Blue, White, and No Collar Jobs

We’re starting to build out a new layer of infrastructure – digital infrastructure – that is both going to “upgrade” our existing world and lay the foundations for the new jobs of the future. This “great digital build-out” will produce next-generation jobs across the spectrum; from civil engineering (digging up roads to put 1,728 fiber count optical cables in place) to structural engineering (keeping the Shard standing) to manufacturing (making the Nvidia DGX-1 and the GE 2.5-120 smart turbine) to services (in Bonobos and from Betterment). Making physical “things” and spaces and places “smart” and connected to the Internet is going to require lots of grunt and grit as well as lots of smarts. And produce lots of jobs.

2. Machines Won’t Replace, but Enhance Human Performance

As most everything around us becomes tech-enabled and connected, we will start applying the ideas behind this “Internet of Things” to mission-critical parts of the economy, such as health care, transportation, and defense. Such a development will begin to radically change – and improve – work that matters. In 2030 we’ll look back at 2017 and wonder how we tolerated the poor quality of medical services that many of us receive today. The same is true of government services, financial service, and education.

Just as we tease our parents and grandparents about the outhouse in the back yard, black-and-white television sets, and cars without seatbelts, our descendants will rib us about how rudimentary and odd our tools are today. They will look back and wonder, “How on earth could they live like that?” In 2030, we’ll laugh as we remember that in 2017 when we visited an A&E we had to sit in a waiting room, then go over our history, instead of having all of that background (as well as pictures and videos of the problem) sent ahead so a team of well-prepared doctors was awaiting us at the door, ready to go.

3. New Tech = Economic Expansion

Making this digital build out come to fruition is the entrepreneurial opportunity of a lifetime. Our current industrial-age inefficiencies may feel terrible now, but the next generation of leaders see these problems as incredible business opportunities. New artificial intelligence-based digital solutions multiplied across virtually every aspect of life will address myriad societal problems, in the process generating enormous economic value. Rather than foreshadowing the end of the middle class, new technology will help drive massive financial expansion.

In thinking about new digital solutions and artificial intelligence, we often focus on the impact of the technology on the world that we know. Many thus go straight to the “How many jobs will machines destroy?” question, yet the question is really about “What can this technology improve?” The answer is “a tremendous amount.” Viewing things from a 2030 perspective, it’s clear how much is about to change.

Enterprises are “becoming digital,” organizing people and processes around the capabilities of the new machines and increasingly finding a new winning formula for the future of their work. When machines do everything there will still be a lot for us to do. Let’s get on with it.