AI and Machine Learning
September 24, 2021
“Any sufficiently advanced technology is indistinguishable from magic.” – Arthur Clarke.
When we think of artificial intelligence (AI), we think of machines, computer systems, algorithms, and models trying to get closer to human intelligence. Today, this space straddles many areas, like natural speech recognition, expert systems, machine learning, general AI applications, and more. The future, however, is mapped towards singularity:
But to get there, a lot of pieces of the AI puzzle would have to come together. Here are a few important ones.
AI is nothing but intelligence that learns, re-learns, and reinforces its learning through the data that it gets. This data can either be specifically fed into the modes or designed to collect this data independently. Hence, the volume and quality of data are extremely important in determining the efficacy and relevance of AI for any purpose.
Big data is a new form of information asset, and it requires massive processing models and the right computing muscle to take care of its flow, velocity, scale, and density. For Big Data to expand and extend human intelligence through AI, we would need to master many dimensions of Big Data. They would include, but would not be limited to, speech recognition, IoT and sensor data, image recognition, unstructured data, and real-time data of many formats.
The space of Big Data is growing rapidly and in multiple directions. As per reports from MarketsandMarkets, it can grow from $138.9 billion in 2020 to $229.4 billion by 2025 and enable companies to increase operational efficiencies and reduce costs. Of course, that should help the growth of AI too.
AI is much more than the fundamentals of the technology that power it. Its real impact and possibility lie in the way it is augmented consistently. It has to get the right research and creative impetus to explore new models, processing modes, algorithm designs, and enablers. There are many constraints for AI, as we see them today. They deter many enterprises from leveraging AI with its full potential. Gartner’s 2019 CIO Agenda survey shows that only 14 percent of global CIOs have already deployed AI. It signified that by 2024, 50 percent of AI investments would be quantified and linked to specific key performance indicators to measure return on investment. About 13 percent struggled with governance issues, and 42 percent feared the unknown in understanding AI uses and benefits. Then there are barriers around vendors with complexity, integration hassles, and capabilities. About 20 percent had privacy concerns, and six percent wondered about the risk of liabilities. Innovation has to cross these barriers and catapult AI to a plug-and-play level for enterprises. Another barrier to AI adoption is quality data (18 percent), as pointed out by the O’Reilly annual AI Adoption in the Enterprise 2021 survey. These barriers will have to be surmounted with the right innovation in academic spheres and the right governance models.
The first reflex thought that most professionals feel when they hear of AI is displacement because of AI. This is not just because AI can take over repetitive jobs. But because AI applications would need a new set of skills and mindset, professionals would have to get ready for it. According to a Gartner Research Circle survey, about 56 percent of respondents felt that acquiring new skills will be required to do both existing and newly created jobs. Interestingly, the O’Reilly annual AI Adoption in the Enterprise 2021 survey says that demand for AI expertise has exceeded supply. Companies are feeling the skills shortage most acutely in the areas of ML modeling and data science (52 percent), understanding business use cases (49 percent), and data engineering (42 percent). Another reflection of the AI skills gap is the finding that the percentage of companies with AI products in production over the last year (25 percent) is flat when compared with 2020 (26 percent) and 2019 (27 percent).
AI is pointless if the intelligence derived here is not fit for the right purpose. About 42 percent of respondents aren’t fully understanding AI benefits and use in the workplace, as per Gartner’s reckoning. Quantifying the benefits of AI projects is another problem for IT and business leaders. When 26 percent of CIOs have a problem finding the use-cases and 25 percent are wrestling with defining the strategy, then a lot remains to be done on the usage aspect of AI.
According to the McKinsey ‘The State of AI in 2020’ report, 50 percent of companies have adopted AI in at least one business function. AI adoption is highest within the product or service development and service-operation functions. In terms of dominant AI use-cases, manufacturing shows 15 percent with yield, energy, and/or throughput optimization and 12 percent with predictive maintenance. In HR, the use is more on optimizing talent management (10 percent) and talent management (7 percent). Marketing and sales are inclined towards customer-service analytics (17 percent) and customer segmentation (14 percent). Strategy and corporate finance areas find their usage in capital allocation (8 percent) and mergers and acquisitions support (6 percent). Logistics-network optimization tops supply chain management with nine percent, and so does inventory and parts optimization (9 percent again).
We have barely scratched the tip of the iceberg here. To fully leverage the power of AI, we need to explore many more applications, but only when we have models and tools that give that kind of confidence.
A lot hinges on how much the top leaders of an organization feel excited about AI. Investments will drive more innovation and more capabilities ahead. For instance, the McKinsey report shows that AI top performers invest more of their digital budgets in AI than their counterparts and are more likely to increase their AI investments in the next three years. As to the distinct aspects of these high performers, there is another point worth noting. The ability to develop AI solutions in-house, instead of buying solutions – and they typically employ more AI-related talent, such as data engineers, data architects, and translators, than their counterparts. These companies have also built a standardized end-to-end platform for AI-related data science, data engineering, and application development. So enterprises would have to find that kind of enthusiasm as budgets to leverage AI strongly.
AI depends a lot on how models use data, learn, and apply their intelligence. That is where AI would need higher and wider processing alternatives. Especially in the area of real-time data. The McKinsey report also talks about the adoption of deep learning—a type of machine learning that uses neural networks and can sometimes deliver superior results. As of now, just 16 percent of companies seem to have taken deep learning beyond the piloting stage. Only high-tech and telecom companies are leading the charge here, with 30 percent embedded deep-learning capabilities. Opening up to new technology paradigms would be a precursor to real growth in AI.
Companies are still not sure about handling a lot of AI ramifications. That’s where the algorithms need to evolve and iron out these kinks. In the McKinsey report, we see how companies are still worried about abort risks like explainability (41 percent), privacy (39 percent), equity and fairness (24 percent), and cybersecurity (62 percent). In fact, as per the O’Reilly survey, mature organizations checked for unexpected outcomes or predictions, interpretability and transparency, and model degradation. Privacy and fairness, bias, and ethics were also ranked above 50 percent in terms of concerns. We need algorithms that can address these issues and bolster the excitement that enterprises have towards AI. If all these ducks fall in a row, AI would swim to new shores of possibility and impact soon.
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Why is AI the hottest buzzword in the Tech World?
AI today is a topic of conversation, a subject for corporate presentations, a space for technological debates; in general, a term uttered by many who are familiar with a machine. AI is a concept that is being adopted by many sectors; indeed the latest and hottest buzz word in the world of technology.
However, the fact is Artificial Intelligence has remained in the nascent stage of development for quite a while. When one hears the word `artificial intelligence’ the visuals of different characters from science fiction come to mind. What was a mythical concept of time travel or spacecraft with extra-terrestrial creatures is not considered a futuristic science anymore? Though still primitive in form, AI can solve unique business problems, provided one knows and decides how to leverage the technology to its potential.
James Goodnight, Co-Founder and CEO of SAS says: `AI is certainly being hyped as another buzzword, and there are many companies rushing to jump on the AI bandwagon’. Despite the hype, AI is a significant emerging technology that will continue to grow in importance. Years ago the term `cloning’ became very popular; and in course of time, was included in the general context and usage, where anything which is replicated was described as `cloned’. Dolly, the sheep (the first mammal cloned from an adult somatic cell) became a widely talked about subject. Along the same lines, people have started using AI to describe any analytics problem, be it faulty equipment or credit card fraud. The catchword now is AI, and Sophia, the Robot has replaced Dolly sheep. With everyone using the buzzword AI, one needs to see whether reality aligns with this hype.
There is so much information on the internet stored in the last few years. Wall Street Journal recently stated that the internet is filling up with good morning messages, and largely by Indians, many of whom are yet new users of the internet! On a more serious note, there are huge quantities of data collected and stored in the recent times. Almost all information is online and accessible to net browsers. A tool to statistically filter, analyze and process large data sets, not merely as a process but with intelligence, is the primary need of industries, investors, for that matter, any establishment across sectors and geographies. A growing technology like AI can unlock the value of data, and eventually help transform businesses, industries, and above all, people’s experiences. It is no surprise that AI being such a powerful concept has become not just a household term, but a word across the technology world.
Though the research in Artificial Intelligence dates back to the 1940s, it is only around the 1980s that AI progressed from research to application. In recent years, however, interest in the field of artificial intelligence has sky-rocketed as evidenced by the amount of funding invested in researching and adopting it.
To go back to sci-fi, we have seen in many robots featuring stories about how the machine ultimately overpowers the man to the point of bringing about total annihilation. This threat could be as real as the creation of artificially intelligent robots. Well, known personalities like physicist Stephen Hawking and Tesla CEO Elon Musk have been stressing on the importance of regulating artificial intelligence.
There is merit in the thought that AI technology will help society in a range of areas like transportation, education, health, manufacturing, retail, arts, medicine and so on. It can be a danger though if used to build weaponry. Leaders across the world have found this worrisome, even terrifying.
One has to admit, turning a buzzword into business is exciting, but like communications specialist Lindsay Kwan says: The danger of artificial intelligence is that we can both overestimate its power today and underestimate its capabilities tomorrow.
Which, between the two, is riskier, is a matter of conjecture.
AI has come to stay high
In the mid-eighties, Small Wonder, a sci-fi TV series entertained adult and child alike. The story revolved around an android in the form of a 10-year-old girl. Looking back, the series was indeed very amateurish and loosely structured. The point is it introduced for the first time, the machine made man to the lay person. Thirty odd years later we hear of Saudi Arabia’s decision to grant citizenship to a robot. Endowed with artificial intelligence, visual data processing and facial recognition, the robot, Sophia is designed to get smarter over time.
The citizenship as such is of hardly any consequence; what it signifies is that Artificial Intelligence (AI) has come of age. Critics have spoken at length about the absurdity of awarding citizenship and an array of rights to a robot, while Saudi ironically is yet highly restrictive about women’s rights. However, many see this as a positive move towards making the country tech savvy and progressive.
In the computing history, man and machine interfaces have only improved and enhanced over a period of time – hence both are not mutually exclusive. Satya Nadella, CEO of Microsoft, said AI, mixed reality, and quantum computing will shape the world in the coming years. The crux of the matter is that the technology world will have a blend of the real and virtual. While current technology enables in us the ability to collect data, Nadella referring to AI says, what is more important is to reason over the data to create intelligence. This is most critical as a differentiator between mechanical equipment and an AI enabled robot. In the mid-fifties the term GIGO (Garbage In Garbage Out) became a descriptive term for computers, primarily because the machines lacked the ability to think for themselves. Whatever output emerged was only as accurate as the information entered. Deep down, computers still are improvised calculators. Recent development in AI comes under the aegis of `machine learning’ and involves programming computers to learn from example data or past experiences and process information based on that.
So how does machine learning impact tasks and get independent of human influence (in terms of execution of task)? Let’s take an example: Automatic transmission in cars has been prevalent for quite some time and in India, they are getting popular by the day. With increasing traffic bottlenecks people are finding it more convenient to drive a car which does not require changing gears. The driver needs to navigate by keeping the steering in control. With AI, autonomous driving, a wide step ahead of automatic transmission, the technology required for a car to accelerate, turn, and brake on real time roads without human intervention is not anymore impossible or far into the future. The recognition that autonomous driving was a prediction problem solvable with machine learning meant that autonomous vehicles could start to become a reality in the marketplace years earlier than has been anticipated.
By breaking down tasks into components, we can begin to see how AI will affect the workplace. AI is usually framed in terms of machines versus humans, but gradually we see it more in terms of understanding the level of judgment necessary to pursue actions. In cases where whole decisions can be clearly defined with an algorithm (for example, image recognition and autonomous driving), we expect to see computers replace humans. This will take longer in areas where judgment cannot be easily described – where there is high level of ambiguity in arriving at decisions or solutions. In other words, where the task is not a mere process with anticipated outcome and the pros and cons fail to fall into previous experience; AI may not accomplish the same without human intervention.
Product companies are all uniformly using AI during their product launches. This has caused excitement as well as anxiety amongst the people. Will AI assist and enable humans to perform better and with ease, or will AI replace humans to the point of redundancy?
With AI moving into the cerebral workspace, it cannot be denied that it will soon take over many tasks hitherto performed by humans. Just as the advent of machines had a significant impact on how people performed physical labor and dispensed with many jobs handled by humans, AI is soon to be responsible for transitioning many of the tasks performed by humans to robot responsibilities.
In the medical field, AI can help improve diagnostics to a very high level, treatment and care will still require human attention and action. Online shopping has rendered many counter sales jobs redundant; but, at the same time courier and door delivery jobs have increased. Apart from these examples, AI is already playing a large role in the areas of music, architecture, education, manufacturing, gaming and just about any sector or industry. In the corporate world (as a quick example) AI has gotten into recruitment – a very important task in any company. Automatic process was only used before to sort resumes based on keywords. Now, many organizations like Ola or Accenture are using bots to streamline their recruitment processes.
As Artificial Intelligence systems evolve and become more and more sophisticated, job displacement is bound to happen. However, interestingly, many new jobs very different from the ones that are currently in existence will be created. Jobs that involve creativity and those that require advanced degrees and highly specialized skill sets will be available alongside the continuous technology transformation.
AI and its Impact on Jobs
The world, at the moment, is awash with speculations, opinions, and fears about artificial intelligence and its perceived `threat’ to human held jobs. Many researchers and scientists are still of the conviction that AI cannot, in actuality, replace human intelligence. They still maintain that AI can assist and augment but never take the place of humans at the workplace.
AI has been an area of passion and pursuit for a long time. However, it is in the recent years that it has come to stay, moving from the research desk to active field. AI is now dominant, taking on an exponential role, influencing particularly the fields of gaming, natural language processing, speech and handwriting recognition and robotics.
Since artificial intelligence is poised to take on many tasks that the human has so far been performing, is there a threat of unemployment across the globe? How is AI going to impact the job scene world over?
Leading research and advisory firm, Gartner comforts job holders in its statement: Robots are not here to take away our jobs; they’re here to give us a promotion. Instead of replacing the human at work, artificial intelligence may increase the number of job opportunities, especially in healthcare, public sector, education, to name a few. As the gig economy is gaining popularity, retail, manufacturing and transportation could possibly be the first to adopt AI for work. Industry heads are not as optimistic as Gartner. Leading personalities Elon Musk and Stephen Hawking feel AI may go beyond taking away people’s jobs. AI could eventually become a real threat to human beings. For now, it is helpful and comforting to go with Gartner’s report which states by 2020, AI will generate 2.3 million jobs, exceeding the 1.8 million that it will remove.
When one of the Big Four consulting firms, Deloitte delved into UK census data for the last 140 plus years only to discover that technology has very steadily taken over many jobs that were being handled by humans. Deloitte also quickly pointed out that jobs in the professional sector have increased multifold.
This pattern of generating new jobs as and when a machine takes over current jobs will continue in this era of AI. Emerging technologies will allow for emerging new jobs, not only for people working with new technologies but also for the enablers who need to manage the increasing requirements of content development and training to bring people up to speed.
The fact is, AI is contributing in a major way to the evolving landscape of work. Going back to the gig economy, the concept is gaining prominent space as it provides scope for professional work within a time-bound period. The big gig companies differ from traditional firms not only in conducting business online but also in allowing and encouraging individuals to freelance and engage `just in time’. As TED CEO Richard Saul Wurman sums up the online marketplace platform: For the first time in human history, individuals can design a life around the pursuit of interesting work.
AI is going definitely going to define the way work happens. How much humans will be involved in `working’ is a subject of speculation for anyone who is familiar with AI. But if we have to think about the next decade, we need to remember though AI is enabling a machine to be independent of man, it is dependent on data. The more data AI consumes the smarter it gets. But to sift data and feed AI and obtain output is a process as laborious as the creation of AI.
Google AI chief John Giannandrea explained this concept of AI’s need to be fed data by using an example involving his 4-year-old daughter. She was able to identify a 19th-century “penny-farthing” bicycle anywhere once he had told her what it was. But computers, need to be shown 100,000 penny-farthings before identifying one. Once they’d seen 100,000, they’d probably be better at identifying them than humans are.
Companies may talk at length about AI and secretly look forward to right-sizing their employee base by bringing in machines. However, they would need to clean up messy databases, work on file management and in overall make their data algorithm-friendly, before any machine can really take over a man’s job. How long will this take? Well, time will tell.
