We are observing an ever-accelerating rate of change in the world around us today. Do you remember complaining about your old computer lagging behind the speed of your creative efforts when you were a teenager? Well, I certainly do, but the reality is, it is us, humans, who actually need to keep up with the rapidly growing capabilities of modern machines at the moment.

Making tomorrow today

In a recent article published on Entrepreneur.com, Elizabeth Gore noted that according to the experts of the Institute for the Future (IFTF) in the Silicon Valley:

85 percent of the jobs of the future haven’t even been invented yet. Meaning, with an aptitude for continual learning and reinvention, and a talent in coding, you can create the future that you want.

It seems, then, that time may soon come when

“work will chase people, rather than the other way around. As an extension of the “gig economy,” organizations will automate how they source work and teams, break up work into tasks and seek out the best talent for a task (by using smart analytics and machine learning to search out individuals’ skills and competencies).”

Back to the future

It is striking, however, to see us now coming full circle, in a sense. Why is that? As Zoë Bernard aptly observed, “through machine learning, technologists have mimicked the way the human brain works by producing sophisticated systems called neural networks. In turn, neural networks enable deep learning, an outcome that has produced computer systems superseding human intelligence.” Okay, but where does all of that lead us?

For example, not only was the AI-based DeepMind’s AlphaGo able to beat the best Go players worldwide some time ago, but the company has even attempted to surpass their own achievement by using an entirely self-taught virtual Go player.

According to Darrell Etherington of techcrunch.com,

“it managed to rediscover over 3,000 years of human knowledge around the game in just 72 hours. It then beat the version of the original AlphaGo that beat champion Lee Sedol in just over three days, and bested the most powerful previous version of AlphaGo ever in just 40 days after that.”

Did, perhaps, the ambivalent “character” of HAL 9000 from the movie “2001: A Space Odyssey” come to your mind right away? Well, to be honest, I am both thrilled and a bit concerned about the pace at which AI and machine learning are currently developing, for that matter. It becomes obvious to me that we, humans, really cannot afford to fall behind.

Sunil Bhatia, CEO at Infogain, said in a recent interview:

My advice to our people is, be a learning machine. The way technology is changing, you have to be a learning machine to survive.”

Wait… Did I get him right? We have created intelligent machines to help us complete some of the most tedious and time-consuming tasks faster and more efficiently, but now we need to become machine-like ourselves in order to keep our finger on the pulse of today’s digital reality. A peculiar twist, to say the least, don’t you think?

A bolt from the blue

As Richard Waters of Financial Times has rightly pointed out recently,

“it is not often that a new profession springs up almost overnight. It is also unusual for many of the people who find their way into this new field to do it without the formal training provided by the normal institutions of higher education. Machine learning, as well as the allied field of data science, is developing in a way that looks unlike most other professional career paths that preceded it. It represents both one of the most promising employment opportunities of the next few years and a model for how people entering the workforce today adapt to changes in employment demands in future.”

From my perspective, the aim of machine learning development is to equip devices with an AI that would be able to learn almost from scratch, instead of programmers feeding every single command and action into it. However, Sunil Bhatia suggests that the current state of play “is absolutely scratching the surface. People are saying the revolution it is s going to bring will be just as significant for human kind as the advent of electricity, industrial revolution or even the Internet.”

A three-ingredient recipe

To me, there are three most important features related to, and even created by, machine learning:

1) A growing number of non-programming and multidisciplinary specialists will be needed in order to hone ML models, which means there will be greater flexibility when it comes to looking for jobs and creating them.

2) Less people will be needed to complete even large projects – Sunil Bhatia claims that “large scale, sheer numbers, headcount, large campuses full of people who can code, etc. are no longer the criteria for clients to pick their partners.” From his point of view, the constantly improving ML technology “does not require 500 people. To do an ML project, you need 10–15 very smart people who understand the space and maybe 20–30 people for other programmers. 50–80 people deals is what is coming.” That is why smaller enterprises are starting to succeed in the competition with the big Tier 1 companies.

3) People will gain even greater autonomy and efficiency at work. This resonates with Elizabeth Gore’s observation that the “IFTF considers the “entrepreneurial mindset” a top competency for individuals in 2030. People will work for companies on the other side of the globe without sacrificing their autonomy. They’ll use artificial intelligence platforms like Alice to seek out collaborators and mentors, access capital, learn about new training, and more.

However, what I find the most intriguing and tempting is what Richard Waters referred to as “lifetime learning,” illustrating it with the example of @Anthony Goldbloom’s Kaggle – a company that “maintains an informal network of experts around the world” and has recently been acquired by Google. Goldbloom himself said it will not be possible to spend an entire working life without re-skilling to adapt to new opportunities.

In a big and small way

Lifetime learning applies not only to individuals but to companies as well. Elizabeth Gore posed a question in this regard, and provided an answer to it: “If pragmatic start-ups face a bright future, how will established, older organizations fare? In 2016, Dell reported that 45 percent of medium- to large-sized companies worry about becoming obsolete within three to five years. IFTF suggests this concern doesn’t have to become reality if they keep pace with AI, fix problems, and offer new services at speed.” Apart from that, companies should also overcome problems identified by Richard Waters, including bad data (“employers cannot provide the essential raw material for [their employees] to obtain results. Some also complain of being given a lack of clear questions to answer. Companies may sense the opportunity, but they often do not know enough to get the most from their data assets.”) Looking at the issue from this perspective, I envision that both the big names and smaller enterprises will be able to co-exist harmoniously in the IT realm, putting ML technologies to good use in ways that simply suit them best.

With a human face

Lastly, I would like to give you some food for thought, so I reach for a brilliant quote by James Scott:

“The human condition is plagued with a labyrinth of shortcomings, frailties, and limitations that hinder a man from reaching his fullest potential. Therefore, it only makes sense that we find ourselves at the next phase in human evolution where restricted man merges with the infinite possibilities of hyper-evolving technologies. This techno-human transmutation will prove to be ‘the’ quantum leap in human progression. The harmonization of technologically extending oneself, consciousness, artificial intelligence, and machine learning will reverse the failures of genetic predisposition and limitation.”

Do you consider the potential of machine learning and AI a blessing or a curse? I am very curious about your feelings and intuitions. Please feel free to comment below.

Share with: