THE VALUE OF AI: now and the future (PART 1) AI Impacts, Risks and Value for Business, Industry, Society and Humanity

This article is first in a 3-part series on THE VALUE OF AI: now and the future —?catch up on the?second and third in the series.?

THE VALUE OF AI: now and the future (3-part series)

(PART 1) AI Impacts, Risks and Value for Business, Industry, Society and Humanity

(PART 2) AI Failures, Pitfalls, Key Learnings and Success

(PART 3) AI Predictions and Future

In this first part of the three-part series on THE VALUE OF AI: now and the future, the article first covers the evolution of AI, what AI is and isn't, the current state of AI, where it is being used today, how it works and how it is changing our world. It highlights the gap between AI high performers and others on realizing and maximizing value from AI. It explores the profound impacts, risks and value of AI for business, industry, society and humanity, along with the importance of ethics, privacy, trust, regulation and responsible AI — with great power comes even greater responsibility. It then concludes with some key findings and takeaways on the impacts, risks and value of AI, as it becomes increasingly embedded deeper?into the fabric of business, industry, society, humanity and our everyday lives.

The age of Artificial Intelligence (AI) is already here and it will eventually change how most businesses, industries, societies and humanity will operate. Even though artificial intelligence was once considered science fiction, AI is increasingly becoming of interest and value to the world of business and technology and if anything is accelerating, alongside with digital transformation and hyper-automation, amidst the COVID-19 pandemic pandemonium. With so much potential, AI could prove to be a powerful tool in addressing certain societal problems, however, as with any new technology, it may cause problems too.

There is a plethora of information on Artificial Intelligence (AI) and Machine Learning (ML). You will not surprisingly find an endless amount of business and technology news, articles, blogs and opinions on AI and ML. The global business, technology, technology advisory and consulting services market is flooded with AI and ML technology, vendors, consultants, platforms, software, tools, services and solutions. It is no wonder why people are constantly getting confused about the meaning, the impacts, risks and value of artificial intelligence in discussions around business and technology trends. The plethora of potential opportunities and the value of AI to business and to humanity are also equally being confused and blurred. Given the information overload and market hype, this then leads to many crucial questions that business leaders, technologists, people and society alike are increasingly asking — what then is AI and what is its value? What are the implications and risks and what does this mean for business, industry, society and humanity?

Let's first take a look back to get some context on the origins — the history of AI — as well as the definition of AI, before we move forward to understanding its application, impacts, risks and value to business, industry, society and humanity; along with the failures, pitfalls, key learnings and success today — the now — and what's next — the future of AI.

Looking Back: History and Evolution of Modern AI

The beginnings of modern Artificial Intelligence can be traced?to classical philosophers' attempts to describe human thinking as a symbolic system. The field of AI research was founded during a summer conference at Dartmouth College (New Hampshire, USA) in 1956, referred to now as the infamous "Dartmouth conference" where John McCarthy, computer and cognitive scientist (the Father of Artificial Intelligence, was a pioneer in the fields of AI), coined the term 'Artificial Intelligence' defined as "the science and engineering of making intelligent machines". This conference attended by 10-computer scientists, saw McCarthy explore ways in which machines can learn and reason like humans.

From the 1950s forward, many scientists, programmers, logicians and theorists aided in solidifying the modern understanding of AI as a whole. With each new decade came new innovations and findings that changed people’s fundamental knowledge of the field of artificial intelligence and how historical advancements have catapulted AI from being an unattainable sci-fi fantasy to a tangible reality for current and future generations. The first AI work however, which is now recognized as AI, was first introduced by Warren McCulloch and Walter Pits in 1943. They proposed a model of artificial neurons. In?the late 1970s and early 1980s, AI research had focused on using logical, knowledge-based approaches rather than algorithms. Additionally, neural network research was abandoned by computer science and AI researchers.

All of the recent developments towards artificial Intelligence and machine learning reflect the emergence of a new field of engineering. As highlighted by Michael I. Jordan (AI and ML leading researcher, University of California) we can draw some parallels with the emergence of chemical engineering in the early 1900s from foundations in chemistry and fluid mechanics. Machine Learning builds on decades of progress in computer science, statistics and control theory. It is the first engineering field that is human-centric, focused on the interface between people and technology.

What is AI? Is everything AI?

Artificial Intelligence is the mantra of the current era. There are too many definitions floating around on what Artificial Intelligence really is. The market has been flooded with technology by vendors with Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) platforms, tools, capabilities and solutions. There is significant misunderstanding accompanying use of the phrase Artificial Intelligence. The term?AI?is misunderstood not only by the public but also by technologists. Is everything AI? What really is AI?

"Stop calling everything AI ... that there is some kind of intelligent thought in computers that is responsible for the progress and which is competing with humans ... we don't have that, but people are talking as if we do"

- Michael I. Jordan, AI and ML leading researcher (University of California)

Michael I. Jordan explains to IEEE Spectrum (2021) why today’s artificial-intelligence systems aren’t actually intelligent. As Michael highlights, "AI systems today are nowhere near advanced enough to replace humans in many tasks involving reasoning, real-world knowledge and social interaction. They are showing human-level competence in low-level pattern recognition skills, but at the cognitive level they are merely imitating human intelligence, not engaging deeply and creatively". He notes that "the imitation of human thinking is not the sole goal of machine learning — the engineering field that underlies recent progress in AI — or even the best goal. Instead, machine learning can serve to augment human intelligence, via painstaking analysis of large datasets in much the way that a search engine augments human knowledge by organizing the Web". Machine learning also can provide new services to humans in domains such as healthcare, commerce, and transportation, by bringing together information found in multiple datasets, finding patterns and proposing new courses of action.

Artificial Intelligence Definition

The short answer to 'What is AI?' is that it depends on who you ask. The average person may link it to robots. They may say something like AI is a Terminator or ExMachina robot like-figure that can act and think on its own. If you ask about artificial intelligence to an AI researcher, they may say that it is "a set of algorithms that can produce results without having to be explicitly instructed to do so".

Back in the 1950s, the fathers in the field, McCarthy and?Minsky, described artificial intelligence as "any task performed by a machine that would have previously been considered to require human intelligence". The Wikipedia definition of AI is "Artificial intelligence?(AI) is intelligence?demonstrated by machines, as opposed to the?natural intelligence displayed by humans or animals". These are fairly broad definitions, which is why you will sometimes see many arguments over whether something is truly AI or not.

Some of the latest, modern definitions of what it means to create intelligence are more specific. Francois Chollet, an AI researcher at Google and creator of the machine-learning software library Keras, has said "intelligence is tied to a system's ability to adapt and improvise in a new environment, to generalize its knowledge and apply it to unfamiliar scenarios". He further says "Intelligence is the efficiency with which you acquire new skills at tasks you didn't previously prepare for". "Intelligence is not skill itself; it's not what you can do; it's how well and how efficiently you can learn new things". It is a definition under which modern AI-powered systems, such as virtual assistants, would be characterized as having demonstrated 'narrow AI', the ability to generalize their training when carrying out a limited set of tasks, such as speech recognition or computer vision.

Typically, AI systems demonstrate at least some of the following behaviours associated with human intelligence: planning, learning, reasoning, problem-solving, knowledge representation, perception, motion and manipulation - and to a lesser extent, social intelligence and creativity.

Here is a modern definition of AI from Great Learning (2021):

• An intelligent entity created by humans.
• Capable of performing tasks intelligently without being explicitly instructed.
• Capable of thinking and acting rationally and humanely.

How do we measure if Artificial Intelligence is acting like a human?

Even if we reach that state where an AI can behave as a human?does, how can we be sure it can continue to behave that way? We can base the human-likeness of an AI entity with the:

• Turing Test
• The Cognitive Modelling Approach
• The Law of Thought Approach

Wikipedia (2021) highlights the Turing Test, originally called the?imitation game?by?Alan Turing?in 1950,?is a test of a machine's ability to exhibit intelligent behaviour?equivalent to, or indistinguishable from, that of a human. Turing proposed that?a human evaluator?would judge natural language conversations between a human and a machine designed to generate human-like responses. If the evaluator cannot reliably tell the machine from the human, the machine is said to have passed the test. To date,?no AI has passed?the Turing test, but some came pretty close.

How does Artificial Intelligence (AI) work?

Despite its growth, it still might be hard for most business leaders and people to grasp how exactly AI works. Building an AI system is a careful process of reverse-engineering human traits and capabilities in a machine and using its computational prowess to surpass what we are capable of.? To understand how AI actually works, one needs to deep dive into the various sub-domains of Artificial Intelligence and understand how these domains could be applied into the various fields of business and industry.

The sub-domains of Artificial Intelligence are as follows:

• Machine Learning (ML): teaches a machine how to make inferences and decisions based on past experience. It identifies patterns, analyses past data to infer the meaning of these data points to reach a possible conclusion without having to involve human experience. This automation to reach conclusions by evaluating data, saves a human time for businesses and helps them make a better decision.
• Deep Learning (DL): is an ML technique. It teaches a machine to process inputs through layers in order to classify, infer and predict the outcome.
• Neural Networks (NN): work on the similar principles as of Human Neural cells. They are a series of algorithms that captures the relationship between various underlying variables and processes the data as a human brain does.
• Natural Language Processing (NLP): is a science of reading, understanding, interpreting a language by a machine. Once a machine understands what the user intends to communicate, it responds accordingly.
• Computer Vision: computer vision algorithms tries to understand an image by breaking down an image and studying different parts of the objects. This helps the machine classify and learn from a set of images, to make a better output decision based on previous observations.
• Cognitive Computing: Cognitive computing algorithms try to mimic a human brain by analyzing text/speech/images/objects in a manner that a human does and tries to give the desired output.

What are the Types of Artificial Intelligence?

Different Artificial Intelligence entities are built for different purposes, and that is how they vary. AI can be classified based on type 1, type 2, type 3 (functionalities). At a very high level, artificial intelligence can be split into three broad types:?

Artificial Narrow Intelligence (ANI) - Narrow AI (or "weak" AI)

Narrow AI is what we see all around us in computers today — intelligent systems that have been taught or have learned how to carry out specific tasks without being explicitly programmed how to do so. This is the most common form of AI that you will find on the market now.?These AI systems are designed to solve one single problem and are able to execute a single task really well. By definition, they have narrow capabilities, like recommending a product for an e-commerce user or predicting the weather.?This is the only kind of Artificial Intelligence that exists today. This type of machine intelligence is evident in the speech and language recognition of the Siri virtual assistant on the Apple iPhone, in the vision-recognition systems of smart cars / self-driving cars, or in the recommendation engines that suggest products you might like based on what you bought in the past. Unlike humans, these systems can only learn or be taught how to do defined tasks, which is why they are called narrow AI. They are able to come close to human functioning in very specific contexts, and even surpass them in many instances, but only excelling in very controlled environments with a limited set of parameters.

Artificial General Intelligence (AGI) - General AI (or "strong" AI)

General AI or AGI is still a theoretical concept.?It is defined as AI which has a human-level of cognitive function, across a wide variety of domains such as language processing, image processing, computational functioning and reasoning and so on. Today, we are still a long way away from building an AGI system. An AGI system would need to comprise of thousands of Artificial Narrow Intelligence systems working in tandem, communicating with each other to mimic human reasoning. Even with the most advanced computing systems and infrastructures, such as Fujitsu’s K or IBM’s Watson, it has taken them 40 minutes to simulate a single second of neuronal activity. This speaks to both the immense complexity and interconnectedness of the human brain, and to the magnitude of the challenge of building an AGI with our current resources.

General AI is very different and is the type of adaptable intellect found in humans, a flexible form of intelligence capable of learning how to carry out vastly different tasks, anything from hair-cutting to building spreadsheets or reasoning about a wide variety of topics based on its accumulated experience.? This is the sort of AI more commonly seen in the movies, the likes of HAL in 2001 or Skynet in The Terminator, but which does not exist today. AI researchers and experts are fiercely divided over how soon it will become a reality.

Artificial Super Intelligence (ASI) - Super AI

We are almost entering into science-fiction territory here, but ASI is seen as the logical progression from AGI.?An Artificial Super Intelligence (ASI) system would be able to surpass all human capabilities. This would include decision making, taking rational decisions, and even includes things like making better art and building emotional relationships. Once we achieve Artificial General Intelligence, AI systems would rapidly be able to improve their capabilities and advance into realms that we might not even have dreamed of. While the gap between AGI and ASI would be relatively narrow (some say as little as a nanosecond, because that is how fast Artificial Intelligence would learn) the long journey ahead of us towards AGI itself makes this seem like a concept that lays light years away, far into the distant future.

How AI works

To reiterate,?Artificial Intelligence is the science of getting machines to think and make decisions like human beings do. Machine Learning is a sub-field of Artificial Intelligence, where we try to bring AI into the equation by learning the input data. Now lets take a closer look at how AI works and where Machine Learning and algorithms fit in.

The simple explanation, an algorithm is a set of steps used to complete a specific task. An algorithm?takes some input and uses mathematics and logic to produce the output. In stark contrast, an?Artificial Intelligence Algorithm takes a combination of both – inputs and outputs simultaneously in order to 'learn' the data and produce outputs when given new inputs. This process of making machines learn from data is what we call?Machine?Learning, as depicted in the diagram below.

Machines can follow?different approaches to learn depending on the dataset and the problem that is being solved.

If we have a look at the capabilities and the power of Artificial Intelligence:

• AI?automates repetitive learning?and discovery through data. Artificial Intelligence performs frequent, high-volume, computerized tasks reliably and without fatigue.
• AI?adds intelligence?to existing products. In most cases, AI will not be sold as an individual application. Rather, products you already use will be improved with AI capabilities, much like Google Assistant was added as a feature to a new generation of smartphones.
• AI?adapts through progressive learning algorithms?to let the data do the programming.?The algorithm becomes a classifier or a predictor. So, just as the algorithm can teach itself how to play any game, it can teach itself what product to recommend next online.
• AI analyzes?more and deeper data?using neural networks that have many hidden layers. You need lots of data to train deep learning models because they learn directly from the data. The more data you can feed them, the more accurate they become.
• AI?achieves incredible accuracy?through deep neural networks, which was previously impossible.?AI techniques from deep learning, image classification and object recognition can now be used to find cancer on MRIs with the same accuracy as highly trained radiologists.

Applications of AI: Where is AI used today?

Whilst we may think that artificial intelligence?is at least a few years away from causing any considerable effects on our lives, the fact remains that it is already having an enormous impact on us. Artificial intelligence is affecting our decisions and our lifestyles every day. There is virtually no major industry modern AI that has not already been affected. That is especially true in the past few years, as data collection and analysis has ramped up considerably thanks to robust Internet of Things (IoT) connectivity, the proliferation of connected devices and speedier computer processing. Whilst some sectors are at the start of their AI journey, others are already veterans. Both have a long way to go. Regardless, the impact AI is having on our present day lives is hard to ignore.

AI is used across industries globally. Some of the industries which have delved deep in the field of AI to find new applications are: Retail, E-Commerce, Banking/Financial Services/Hedge Funds, Security and Surveillance, Sports Analytics, Manufacturing and Production, Automotive, among others.? AI truly has the potential to transform many industries, with a wide range of possible use cases. What all these different industries and use cases have in common, is that they are all data-driven. Since AI is an efficient data processing system at its core, there is a lot of potential for optimization everywhere.

Artificial Intelligence in our Everyday Lives

There are various applications of Artificial Intelligence across industries, here are a few of the important ones that are already present in our daily tasks and lives.

• Smart Phones

If you are using a smartphone, you are interacting with AI whether you know it or not. From the obvious AI features such as the built-in smart assistants to not so obvious ones such as the portrait mode in the camera. Have you ever thought how the Google Pixel phones or iPhones can capture such great portrait shots? The answer is artificial intelligence.

• Speech Recognition

• Machine Translation

• Facial Recognition and Automatic Tagging

• Virtual Personal Assistants

• Chatbots

Chatbots is another common example, based on the proliferation of AI chatbots?across industries and every other website we seem to visit. These chatbots are now serving customers at odd hours and peak hours as well, removing the bottleneck of limited human resources.

• Robotics

The field of robotics has been advancing even before AI became a reality. Today, AI is helping robotics to innovate faster with efficient robots. Robots in AI have found applications across verticals and industries especially in the manufacturing, packaging, e-commerce and the supply chain.

• Security and Surveillance

• Social Media feeds

Even if you are living under a rock, there is a high probability that you are tweeting from underneath it. If Twitter is not your choice of social media, maybe its Facebook?or Instagram, or Snapchat or any other of the myriad of social media apps out there. Well,?if you are using social media, most of your decisions are being impacted by artificial intelligence.

• Digital Advertising - Online Ads

One of the biggest users of artificial intelligence is the online ad industry which?uses AI to not only track user statistics but also serve us ads based on those statistics. Without AI, the online ad industry will just fail as it would show random ads to users with no connection to their preferences whatsoever. AI has become so successful in determining our interests and serving us ads that the global digital ad industry has crossed US $300 billion with the industry projected to double and cross the US $600 billion mark by 2024. I do wonder just how much of this social media and digital ad revenue and the powerful far-reaching and subtle manipulation — by those funding and enabling it — has impacted political campaigns, elections and referendums with detrimental impacts to society and humanity (eg, UK Brexit vote, US 2016 presidential election with Cambridge Analytica / Facebook scandal), racism, terrorism, extremism, societal political divide, violence, etc.? So next time when you are going online and seeing ads or product recommendations (marketing ads, political/election ads, etc.), know that AI is impacting your life.

• Video Games

The video game or gaming industry is probably one of the earliest adopters of AI. The integration started very small with the use of AI to generate random levels that people can play. However, that has increased to a level which goes far beyond what one can even imagine. Just know that if you play any game, you are using AI.

Artificial Intelligence in Business and Industries

Since the modern day advancements of narrow AI and ML capabilities that we have today leveraging complex algorithms, we have been able to create machines and robots that are applied in a wide range of fields and industries including e-commerce, agriculture, banking, financial services, healthcare, retail and many more. We will now further explore current applications of artificial intelligence across industries.

AGRICULTURE

The agriculture industry is turning to AI to help yield healthier crops, control pests, monitor soil and growing conditions, organize data for farmers, help with the workload and improve a wide range of agriculture-related tasks in the entire food supply chain. While using the machine learning algorithms in connection with images captured by satellites and drones, AI-enabled technologies are helping the agricultural industry?predict weather conditions, analyze crop sustainability and evaluate farms for the presence of diseases or pests and poor plant nutrition on farms with data like temperature, precipitation, wind speed and solar radiation.

AUTOMOTIVE

Smart Cars, Robots and Driver-less Cars

Tesla cars are a prime example of how the AI is impacting our daily life.?Did you know that all the Tesla cars are connected and the things that your car learns is shared across all the cars? That means, if you had to take an unanticipated hard-left on a cross-road, all the Tesla cars will know how to maneuver that turn after they are updated. There are already more than 500,000 Tesla cars?running in the US alone and that number is set to increase exponentially now that Tesla has solved its major production problems.

The desire for robots to be able to act autonomously and understand and navigate the world around them means there is a natural overlap between robotics and AI. While AI is only one of the technologies used in robotics, AI is helping robots move into new areas such as self-driving cars, delivery robots and helping robots learn new skills. At the start of 2020, General Motors and Honda revealed the Cruise Origin, an electric-powered driverless car and Waymo, the self-driving group inside Google parent Alphabet, recently opened its robotaxi service to the general public in Phoenix, Arizona,?offering a service covering a 50-square mile area in the city.

BANKING AND FINANCIAL SERVICES

The strategic application of AI — including machine learning, natural language processing and computer vision — can drive meaningful results for banks, from enhancing employee and customer experiences to improving back-office operations. Cost savings connected to the use of AI can be significant. With its power to predict future scenarios by analyzing past behaviours, AI?helps banks predict future outcomes and trends. This also helps banks to identify fraud, detect anti-money laundering pattern and make customer recommendations.

Financial Trading, Stock Markets and Hedge Funds

It is no secret that trading robots have been working in the stock market for a long time, especially over the last two decades, focusing on price movements in trends and within channels. For 30 years quantitative investing started with a hypothesis. Investors would test it against historical data and make a judgment as to whether it would continue to be useful. Ventures have been relying on computers and data scientists to determine future patterns in the market. Trading mainly depends on the ability to predict the future accurately. According to The Economist (2020) the role humans play in trading has diminished rapidly. In their place have come advanced computing, AI-powered algorithms and passive managers —institutions which offer an index fund that holds a basket of shares to match the return of the stockmarket, or sectors of it, rather than trying to beat it. Whilst algorithms have been used in the stock markets over the last few decades, more recently AI-powered algorithms have supercharged the stock market with sky-rocketing gains and profits.

Enter data science and AI-powered algorithms with machine learning, now the order has been reversed, funds start with the data and look for a hypothesis. Humans are not out of the picture entirely. Humans role is to pick and choose which data to feed into the machine. “You have to tell the algorithm what data to look at”, says an investor reported via the Economist (2019). The Economist (2019) highlights that on September 13 2019 a widely watched barometer published by Morningstar, a research firm, reported that for the first time, the pot of passive equity assets [run by funds leveraging AI-powered algorithms] measures, at US $4.3trn ($ Trillions), exceeded that run by the traditional active funds. These passive funds are also known as 'quant funds' which are investment funds whose securities are chosen based on numerical data compiled through quantitative analysis - these funds are considered non-traditional and passive.?The rise of financial robotization and use of artificial intelligence is not only changing the speed and makeup of the stockmarket. It also raises some rather alarming and concerning questions about the function of markets, the impact of markets on the wider economy, how companies are governed and financial stability.

A prism by which to see the progress of AI-powered algorithmic investing is hedge funds.?Hedge funds are investment funds, financial organizations that raise funds from investors and manage them. They usually work with time series data and try to make some predictions. Cerulli Edge in their Aug 2020 global edition, highlights that hedge funds with artificial intelligence capabilities showed a huge competitive edge over investors that did not use AI new research indicates.?Indeed, a class of AI pure play hedge funds has emerged in recent years that are based entirely on machine learning and AI-powered algorithms. Examples include?Aidiyia Holdings, Cerebellum Capital, Taaffeite Capital Management and Numerai. "An examination by Cerulli Associates of the assets under management (AUM) of various funds and net new flows of Europe-domiciled AI-enabled funds from 2013 to April this year reveals substantial AUM growth from 2016 to 2019. The aggregate return of AI-led hedge funds was almost three times higher than that of the overall hedge fund market during this time: 33.9% compared to 12.1%".?

The result is that the stockmarket is now hyper-efficient. The new AI powered robo-markets bring much lower costs. According to the Economist (2019) Passive funds charge 0.03-0.09% of assets under management each year. Traditional funds with active managers often charge 20 times as much. Hedge funds, which use leverage and derivatives to try to boost returns further, take 20% of returns on top as a performance fee which is staggering. Now you can see that the financial corporations and hedge funds who refer to it as the “scientific approach”, using data and AI-powered algorithms, giving them seemingly tremendous superpowers and edge in the financial stock markets, supercharging their profits.

The global pandemic sent further shockwaves throughout the global economy further triggering demand for forecasting market volatility and identifying the best market opportunities within the total chaos in various markets worldwide and AI-powered algorithmic solutions appeared to be a total game-changer for investors community around the globe not only to preserve their investments’ value, but even shift them to a completely new risk-to-return levels. In the age of ultra-high-frequency trading, financial organizations are turning to AI to improve their stock trading performance and boost profits. According to a 2020 JPMorgan study, over 60% of trades over $10M were executed using AI powered algorithms. The AI powered algorithmic trading market is expected to grow by $4 billion by 2024, bringing the total volume to $19 billion. Money never sleeps — the stockmarket is now run by AI-powered algorithms and passive managers. It is therefore significant and concerning that algorithms untethered from human decision-making are starting to call the shots. “Public markets are becoming winner-takes-all,” complains one of the world’s largest asset managers. “I don’t think we can even come close to competing in this game,” (The Economist, 2019).?And there remains a genuine fear, "what happens if quant funds fulfill the promises of their wildest boosters?" Stockmarkets are central to modern economies.

Bloomberg (2021) highlights that AI-powered Passive funds and asset vehicles’ lead in the overall US $11.6 trillion domestic equity-fund market will likely expand. According to Bloomberg (2021) passive overtook active around August 2018 and its market share today stands at about 54%; passive assets stand at $6.2 trillion today less than a sixth of the U.S. stock market - driven largely by the growth of funds tracking the S&P 500, the total U.S. stock market and other broad U.S. indexes. U.S. large-cap stocks are widely recognized as comprising the world’s most efficient equity market, contributing to passives' dominance. Today, the passive assets still accounts for less than a sixth of the U.S. stock market, with its market cap of about $40.4 trillion. Bloomberg (2021) projects that passive assets are likely to overtake active by 2026, or earlier if bear market.

AI-driven algorithmic investing often identifies factors that humans have not. The human minders may seek to understand what the machine has spotted to find new “explainable” factors. Such new factors will eventually join the current ones. But for now and seemingly for a long while yet they will give an advantage to those who hold them unless AI regulation AND tighter financial transparency, controls and regulations — if and when that takes hold.

BIOTECH/PHARMACEUTICALS

Using AI-powered tactics in the?Biotech and Pharma industry?means?using?automated algorithms to handle tedious tasks performed by humans.?AI has streamlined and impacted the?biotech and pharmaceutical industry?in many ways, ranging from creating new drugs to combating fast-growing diseases. The recent breakthrough by Google's AlphaFold 2 machine-learning system is expected to reduce the time taken during a key step when developing new drugs from months to hours.

In the Deep Pharma Intelligence (2020) report they have profiled 240 actively developing AI-driven biotech startups, adding some 40 companies since their 2019 edition. A steady growth in the “AI for Drug Discovery sector” can be observed in terms of substantially increased amount of venture capital pouring into the AI-driven biotech startups (around $1.9B in 2020 alone, so far), the increasing number of research partnerships between leading pharma organizations and AI-biotechs and AI-technology vendors, a continuing pipeline of industry developments, research breakthroughs, as well as exploding attention of leading media and consulting companies to the topic of AI in pharma and healthcare. Some of the leading pharma executives increasingly see AI as not only a tool for lead identification, but also a more general tool to boost biology research, identify new biological targets and develop novel disease models.

E-COMMERCE

E-Commerce product recommendations is usually the first example that people give when asked about business applications of AI and that is because it is an area where AI has delivered great results already. The use of artificial intelligence in online shopping has profoundly transformed the E-commerce industry?by predicting shopping patterns based on the products that shoppers buy and when they buy them. Most large e-commerce players such as Amazon, Alibaba, eBay, etc. have incorporated AI to make product recommendations that users might be interested in, which has led to considerable increases in their bottom-line.

Amazon and Artificial Intelligence

Ever wondered why Amazon has been so successful with e-commerce (more than many others)? Jeff Bezos and Amazon was one of the first companies to build their business around AI and machine learning, Amazon has always had a significant competitive advantage. Amazon significantly invested, adopted and embedded narrow AI, ML and robotics early on across its entire business and business model and has not stopped its growth, innovation and sky-rocketing profits and market domination ever since — all powered by AI. Amazon is a company that has reorganized and restructured itself to leverage artificial intelligence in every part of the organization - integrating AI from Top to Bottom. Amazon’s recommendation engines are now driving 35% of total sales. Not only has it been using AI to enhance its customer experience but has been heavily focused internally. From using AI to predict the number of customers willing to buy a new product to running a cashier-less grocery store, Amazon's AI capabilities are?designed to provide customized recommendations to its customers.

So, what is Amazon’s secret sauce, besides being an early adopter and being powered with AI across the entire business? How have they integrated artificial intelligence into their business so successfully? Read the next part — (PART 2) AI Failures, Pitfalls, Key Learnings and Success to find out the secret sauce.

HEALTHCARE

The use of artificial intelligence has given a new dimension to healthcare. With the introduction of AI-powered machines, it is becoming a bit easier to?detect disease and diagnosis. AI is also playing a significant role in making the treatment and management processes more simplified. As a result, hospitals and healthcare centers are fast embracing AI-enabled technologies to facilitate everything from research to the detection of diseases. AI could eventually have a dramatic impact on healthcare, helping radiologists to pick out tumors in x-rays, aiding researchers in spotting genetic sequences related to diseases and identifying molecules that could lead to more effective drugs.

There have been trials of AI-related technology in hospitals across the world. These include IBM's Watson clinical decision support tool, which oncologists trained at Memorial Sloan Kettering Cancer Center, and the?use of Google DeepMind systems by the UK's National Health Service, where it will help spot eye abnormalities and streamline the process of screening patients for head and neck cancers.

RETAIL

AI in retail involves?the use of automation, data, and technologies such as machine learning (ML) algorithms?to deliver highly personalized shopping experiences to consumers. AI can be applied to consumer experiences in both physical and digital stores. Using AI algorithms, retail businesses can?run targeted marketing campaigns based on customers' region, preferences, gender and purchasing habits. It will help in improving customer loyalty and retention as a personalized experience is a great way to show them care.

The main benefits of using AI / ML in Retail:

• Lower cost-to-serve as AI transforms the workforce. While routine tasks are being automated, AI will also support and enhance increasingly demanding work.
• More effective promotion and merchandising.
• A better, and richer, more personalized customer experience.

AI is enabling?retail to optimize customer experiences, forecasting, inventory management and more. AI in retail can offer personalized shopping experience to customers. Technologies like biometric and face recognition can identify customers revisiting a store and remember their likes and dislikes. AI can be trained to leverage individual behaviours, preferences, fears, beliefs and interests to personalize experiences. AI can also automate in-store operations and reduce operational expenses in retail stores. It can replace sales personnel to assist customers in the store, reduce queues through cashier-less payments and self-service checkouts, replenish stock by real-time stock monitoring, digitize store displays and trial rooms. In a price-sensitive market like retail, AI can provide valuable information for pricing strategy, helping retailers to analyze the efficacies of multiple pricing models before arriving at the optimal price for their products.?

Retailers around the world lose money every year due to inefficient inventory planning. AI-enabled logistics management can predict demands for products by scrutinizing historical sales, location, buying trends, etc. We have recently developed AI-powered drones for warehouse management that can reach difficult corners and automatically update a central database with available inventory in real time. As you can see, all aspects of the retail supply chain, including inventory, staffing, distribution and delivery, can be managed in real-time by implementing artificial intelligence.

AI REGULATIONS, ETHICS, TRUST, TRANSPARENCY AND REPUTATION

When you use AI to support business-critical decisions based on sensitive data, business leaders and organisations need to ensure that they understand what AI is doing, and why. Is it making accurate, bias-aware decisions? Is it violating anyone’s privacy? Can you govern and monitor this powerful technology? Globally, organisations recognize the need for Responsible AI but are at different stages of the journey.

AI Regulations and Compliance

As AI becomes increasingly embedded?into the fabric of business, society and our everyday lives, both corporations and consumer-advocacy groups have lobbied for clearer rules to ensure that it is used fairly. In May 2021, the European Union (EU) Commission became the first governmental body in the world to issue a comprehensive response in the form of draft regulations aimed specifically at the development and use of AI (the 'AI Regulation') which it describes as “the first ever legal framework on AI”. The proposed regulations would apply to any AI system used or providing outputs within the European Union, signaling implications for organizations around the world. The proposed EU rules are just one more step toward global AI regulation. The AI Regulation will impose significant obligations impacting businesses across many, if not all, sectors of the economy.?The AI Regulation will prove controversial, touching off a legislative battle lasting at least until 2022.?

Innovate Responsibly

According to Deloitte (2020) for most organisations, AI ethical risks are not a top-of-mind information technology concern. And while ethical risks ranked at the bottom of risk concerns in the Deloitte survey, about a third of executives did cite them as a top concern. In a deeper look at potential ethical risks, surveyed executives revealed a wide range of concerns. At the top of the list is AI’s power to help create or spread false information. Some of the ethical risks that resonated with Deloitte's (2020) respondents are linked to cyber-safety and regulatory issues: unintended consequences, misuse of personal data and lack of explanation for AI-powered decisions. But there is one concern that has achieved special prominence in recent years, and ranked second among respondents’ ranking of ethical risks: bias. Today, algorithms are commonly used to help make many important decisions, such as granting credit, detecting crime, and assigning punishment. Biased algorithms, or machine-learning models trained on biased data, can generate discriminatory or offensive results.

Technological Social Responsibility

Given the potential for a win–win across business, industries and society from a socially careful and innovation-driven adoption strategy, the time has come for business leaders across sectors to embed a new imperative in their corporate strategy — technological social responsibility (TSR). It amounts to a conscious alignment between short-term and medium-term business goals and longer-term societal ones. By aligning business and societal interests along the twin axes of innovation focus and active transition management, technology adoption can potentially increase productivity and economic growth in a powerful and measurable way. In economic terms, innovation and transition management could, in a best-case scenario, double the potential growth in welfare—the sum of GDP and additional components of well-being, such as health, leisure and equality.

AI and Data Science Ethics

While the potential ethical issues that might arise when using data science and artificial intelligence have been in the media recently, there has not been as much discussion with respect to organisations and how a data science team?should incorporate ethics within a project. According to the Data Science Process Alliance (2021) many data scientists still do not focus on the ethical conundrums that they might encounter. To help ensure ethics is considered during a data science project, the Data Science Process Alliance recommends that executives and leaders ask ten questions to identify and address data science ethical conundrums in their organisations.

Responsible AI

According to PWC (2021) Responsible AI?is the only way to mitigate AI risks. Now is the time to evaluate your existing practices or create new ones to responsibly and ethically build technology and use data, and be prepared for future regulation. Future payoffs will give early adopters an edge that competitors may never be able to overtake.

Responsible AI is a?governance framework?that documents how a specific organization is addressing the challenges around AI from both an ethical and legal point of view. Currently at the time of writing this article, there are no local or global regulations or legislation around AI. The development of fair, trustworthy AI standards is entirely up to the discretion of organisations and their data scientists and software developers who write and deploy specific AI algorithmic models. This means that the steps required to prevent discrimination and ensure transparency vary from company to company.

Just as ITIL?provided a common framework for delivering IT services, proponents of responsible AI want a widely adopted governance framework of AI best practices to make it easier for organizations around the globe to ensure their AI programming is human-centered, interpretable and explainable.?

Responsible AI is an emerging area of AI governance?and use of the word "responsible" is an umbrella term that covers both ethics and democratization. Democratization meaning making it accessible to everyone.

The heads of Microsoft and Google have?publicly called for AI regulations, but as of this writing, there are no standards for accountability when AI programming creates unintended consequences. Often, bias can be introduced into AI by the data that's used to train machine learning models. When the training data is biased, it naturally follows that decisions made by the programming are also biased.

Now that software programs with AI features are becoming more common, it is increasingly apparent that there is a need for standards in AI. The technology can be misused accidentally (or on purpose) for a number of reasons - and much of the misuse is caused by a?bias in the selection of data?to train AI programming.

What are the principles of responsible AI?

TechTarget (2021) highlight the key principles of responsible AI — AI and the machine learning models that support it should be comprehensive, explainable, ethical and efficient.

• Comprehensiveness?– comprehensive AI has clearly defined testing and governance criteria to prevent machine learning from being hacked easily.
• Explainable AI - is programmed to describe its purpose, rationale and decision-making process in a way that can be understood by the average end user.
• Ethical AI - initiatives have processes in place to seek out and eliminate bias in machine learning models.
• Efficient AI - is able to run continually and respond quickly to changes in the operational environment.

Why is responsible AI important?

An important goal of responsible AI is to reduce the risk that a minor change in an input's weight will drastically change the output of a machine learning model.

Within the context of conforming to the four tenets of corporate governance, responsible AI should be:

• Each step of the model development process should be recorded in a way that cannot be altered by humans or other programming.
• The data used to train machine models should not be biased.
• The analytic models that support an AI initiative can be adapted to changing environments without?introducing bias.
• The organization deploying AI programming is sensitive to its potential impact -- both positive and negative.

How do you design responsible AI?

Building a responsible AI governance framework can be a lot of work. Ongoing scrutiny is crucial to ensure an organization is committed to providing an unbiased, trustworthy AI. This is why it is crucial for an organization to have a maturity model or rubric (assessment tool) to follow while designing and implementing an AI system.

At a base level, to be considered responsible, AI must be built with resources and technology according to a company-wide development standard that mandates the use of:

• Shared code repositories
• Approved model architectures
• Sanctioned variables
• Established bias testing methodologies to help determine the validity of tests for AI systems
• Stability standards for active machine learning models to make sure AI programming works as intended

Responsible AI should include the qualities and principles listed in the diagram above.

• Ensure data is explainable in a way that a human can interpret
• Ensure design and decision-making processes are documented to the point where if a mistake occurs, it can be?reverse-engineered?to determine what transpired.
• Build a diverse work culture and promote constructive discussions to help mitigate bias.
• Use?interpretable latent features?to help create human-understandable data.
• Create a rigorous development process that values visibility into each application's latent features.

TechTarget (2021) provide some best practices for responsible AI that organisations can adopt. They also provide some examples on what other organisations have done to adopt enterprise-wide Responsible AI principals and standards.

ARTIFICIAL INTELLIGENCE IMPACTS, RISKS AND VALUE

How will AI change the world?

Prominent leaders across business, technology, science, academia, governments, countries and broader society are all keeping their eye on the rapid evolution of AI and assessing its potential impacts, risks and value and what it means for business, industry, society and humanity.

“AI is going to change the world more than anything in the history of mankind. More than electricity.”

— Dr. Kai-Fu Lee, AI oracle and venture capitalist, 2018

AI Impacts, Risks and Value for Business and Industry

Artificial Intelligence?is seen by many business, tech and industry leaders as a great transformative?technology?that will change the way we work, live, and interact.

AI Impacts - for Business and Industry

For business and industry leaders, they will need to understand and be convinced of the argument that proactive management of technology transitions is not only in the interest of society at large but also in the more narrowly focused financial interest of companies themselves.

When organisations use AI to support their business-critical decisions based on sensitive data, they need to ensure that they understand what AI is doing, and why. Often, bias can be introduced into AI by the data that is used to train machine learning models.

What can CEOs and their top management teams do to lead the way on bias and fairness? According to HBR (2019) there are six essential steps:

1. First, business leaders will need to stay up to-date on this fast-moving field of research.?
2. Second, when your business or organization is deploying AI, establish responsible processes that can mitigate bias. Consider using a portfolio of technical tools, as well as operational practices such as internal “red teams,” or third-party audits.?
3. Third, engage in fact-based conversations around potential human biases. This could take the form of running algorithms alongside human decision makers, comparing results, and using “explainability techniques” that help pinpoint what led the model to reach a decision – in order to understand why there may be differences.?
4. Fourth, consider how humans and machines can work together to mitigate bias, including with “human-in-the-loop” processes.?
5. Fifth, invest more, provide more data and take a multi-disciplinary approach in bias research (while respecting privacy) to continue advancing this field.
6. Finally, invest more in diversifying the AI field itself. A more diverse AI community would be better equipped to anticipate, review, and spot bias.

AI Risks - for Business and Industry

Executives are concerned about a host of risks associated with AI technologies. Some of the risks are typical of those associated with any information technology; others are as unique as AI technology itself. A recurring theme in the McKinsey (2020) report is the importance of the establishment of ethics boards at companies that rely on AI, whether as a service or a product. This is mentioned particularly for businesses that plan to record and analyze workplace conversations: Boards with employee representation should be established to ensure fair use of conversations data.

Smart organizations are preparing for compliance—and managing AI risk. McKinsey (2020) highlight that AI high performers remain more likely than others (the rest) to recognize and mitigate most risks. For example, respondents at high performers are 2.6 times more likely than others to say their organizations are managing equity and fairness risks such as unwanted bias in AI-driven decisions. Gartner (2021) also recommends that businesses establish criteria for responsible AI consumption and prioritize vendors that "can demonstrate responsible development of AI and clarity in addressing related societal concerns". As for security concerns surrounding deepfakes and generative AI, Gartner (2021) recommends that organizations should schedule training about deepfakes. "We are now entering a zero-trust world. Nothing can be trusted unless it is certified as authenticated using cryptographic digital signatures", the report said.?

At a time when many companies are looking to deploy AI systems across their operations, being acutely aware of AI risks and working to reduce them is an urgent priority.? However, McKinsey (2020) research shows that many organizations still have a lot of work to do to prepare themselves for regulation and address the risks associated with AI more broadly. In 2020, only 48% of organizations reported that they recognized regulatory-compliance risks, and even fewer (38%) reported actively working to address them. Far smaller proportions of the companies surveyed recognized other glaring risks, such as those around reputation, privacy and fairness. These statistics are alarming given the well-publicized incidents in which AI has gone awry and because AI-related regulations, such as Europe’s General Data Protection Regulation (GDPR), already exist in parts of the world.

A lack of AI model explainability presents a level of risk in nearly every industry. According to McKinsey (2020) explainability can present another risk: lack of AI adoption, leading to wasted investment and the risk of falling behind the competition. AI algorithms that ingest real-world data and preferences as inputs may run a risk of learning and imitating possible biases and prejudices.

Performance risks include:

• Risk of errors
• Risk of bias and discrimination
• Risk of opaqueness and lack of interpretability
• Risk of performance instability

Most companies are still not acknowledging most AI risks. The McKinsey (2020) Global Survey on AI findings suggest that only a minority of companies recognize many of the risks of AI use, and fewer are working to reduce the risks — as was true in 2019. Cybersecurity remains the only risk that a majority of respondents say their organizations consider relevant. Overall, the share of respondents citing each risk as relevant has remained flat or has decreased, with the exception of national security. Yet some of the less commonly considered risks are the ones in which we see increasing mitigation. Responses also indicate that companies increasingly manage risks related to AI explainability. Overall, however, McKinsey's (2021) results around AI risk mitigation are concerning. While some risks, such as physical safety, apply to only particular industries, it is difficult to understand why universal risks are not recognized by a much higher proportion of respondents.

"It’s particularly surprising to see little improvement in the recognition and mitigation of this (AI) risk given the attention to racial bias and other examples of discriminatory treatment such as age-based targeting in job advertisements on social media".

- Roger Burkhardt, Partner, McKinsey (New York)

AI Value - for Business and Industry

The results from McKinsey's (2020) Global Survey on AI suggest that organizations are using AI as a tool for generating value. Increasingly, that value is coming in the form of revenues. A small contingent of respondents coming from a variety of industries attribute 20% or more of their organizations’ earnings before interest and taxes (EBIT) to AI. These companies plan to invest even more in AI in response to the COVID-19 pandemic and its acceleration of all things digital. This could create a wider divide between AI leaders and the majority of companies still struggling to capitalize on the technology; however, these leaders engage in a number of practices that could offer helpful hints for success.

Leading consulting companies highlight that there exists a huge gap between AI high performers and others on realizing and maximizing value from their AI investments, as follows:

• According to Deloitte (2020) to maximize value, early adopters should become risk and change management experts.
• "Companies capturing lasting value from artificial intelligence think differently, from the C-suite to the front line" (McKinsey, 2021).?
• According to a major study released by?MIT Sloan Management Review and BCG (2020), despite increased investment and activity, only 10% of organizations are achieving significant financial benefits with AI. The study highlights the often underestimated role of mutual learning between humans and machines in generating value from AI. Those companies that draw on multiple types of interaction and feedback between humans and AI are?6X?times more likely to amplify their success with AI.

“The single most critical driver of value from AI is not algorithms, nor technology —it is the human equation"

- Shervin Khodabandeh, Senior Partner and Managing Director (BCG)

While the latest McKinsey (2020) findings show no increase in AI adoption, some companies are capturing value from AI at the enterprise level and many are generating revenue and cost reductions at least at the function level. The business functions in which organizations adopt AI remain largely unchanged from the McKinsey 2019 survey, with service operations, product or service development, marketing and sales again taking the top spots. By industry, respondents in the high-tech and telecom sectors are again the most likely to report AI adoption, with the automotive and assembly sector falling just behind them (down from sharing the lead last year).

One of the most remarkable patterns in the McKinsey (2020) findings is the adoption of core practices among companies capturing value from AI. Companies seeing the highest bottom-line impact from AI exhibit overall organizational strength and engage in a clear set of core best practices. McKinsey (2020) highlights the six sets of practices that differentiate high-performing companies from others, with a subset adopted much more often by these leaders. The six sets of practices are: (1) Strategy; (2) Talent and Leadership; (3) Ways of Working; (4) Models, tools and technology; (5) Data; and (6) Adoption.

The COVID-19 effect — despite the economic challenges that pandemic-mitigation measures have caused for many companies, according to McKinsey (2020) those seeing the most value from AI are doubling down on the technology. The companies seeing significant value from AI are continuing to invest in it during the pandemic. Most high-performing companies have increased their investment in AI amid the COVID-19 crisis, McKinsey (2020) further highlights that "some of the biggest gaps between AI high performers and others aren’t only in technical areas, such as using complex AI-modeling techniques, but also in the human aspects of AI, such as the alignment of senior executives around AI strategy and adoption of standard execution processes to scale AI across an organization". According to McKinsey (2020) higher performers develop or heavily customize their AI capabilities in-house. Many executives now realize that AI solutions typically need to be developed or adapted in close collaboration with business users to address real business needs and enable adoption, scale and real value creation. As a result, we see companies increasingly developing a bench of AI talent and launching training programs to raise the overall analytics acumen across their organizations.

AI Impacts, Risks and Value for Society and Humanity

AI Impacts - for Society and Humanity

Companies and industries are making significant profits and revenue from AI / ML, that is projected to accelerate and exponentially increase (mostly profit-driven) with far-reaching impacts on society and our everyday lives. Artificial Intelligence is already affecting our decisions and is impacting our daily lives. It is very pervasive, far-reaching and will only continue to proliferate, becoming even further embedded into our society and every aspect of our lives.

Government, people, society and humanity have been primarily distracted with the pandemic pandemonium, lockdowns and COVID-19 vaccinations (for the past 18+ months), meanwhile the advances of AI adoption in business has accelerated and its impacts and risk to society and humanity are currently not on the radar. Whilst business and industry if anything have taken a quantum leap — by several years — and used the impetus of the past year and a half to accelerate digital transformation, automation and the adoption of AI-powered algorithms.

AI will be an accelerant for further economic inequality in our society. While it is a competitive advantage for those who can harness it for data-driven decision-making, AI remains most accessible to the very well-resourced due to highly compensated practitioners. The financial rewards are increasingly consolidated to a few wealthy individuals while AI detrimentally disrupts the global economy, employment/unemployment levels and labour markets, society and humanity.

AI / ML development is contributing to increases in global warming. As the size of machine-learning models and the datasets used to train them grows, so does the carbon footprint of the vast compute clusters that shape and run these models. The environmental impact of powering and cooling these compute farms was?the subject of a paper by the World Economic Forum in 2018. One?2019 estimate was that the power required by machine-learning systems is doubling every 3.4 months. As demand for services based on these models grows, power consumption and the resulting environmental impact becomes an issue.

AI Risks - for Society and Humanity

The Gartner (2021) report predicts that the second-order consequences of widespread AI will have massive societal impacts, to the point of making us unsure if and when we can trust our own eyes. Gartner's findings and predictions when combined create a picture of a grim future rife with ethical concerns, potential misuse of AI and loss of privacy in the workplace.?

Reinforcing discrimination and bias?

A growing concern is the way that machine-learning systems can codify the human biases and societal inequities reflected in their training data. These fears have been borne out by multiple examples of how a lack of variety in the data used to train such systems has negative real-world consequences.? In 2018, an?MIT and Microsoft research paper?found that facial recognition systems sold by major tech companies suffered from error rates that were significantly higher when identifying people with darker skin, an issue attributed to training datasets being composed mainly of white men. Another?study a year later?highlighted that Amazon's Rekognition facial recognition system had issues identifying the gender of individuals with darker skin, a?charge that was challenged by Amazon executives, prompting?one of the researchers to address the points raised in the Amazon rebuttal.

A Double-Edged Sword For Crime Prevention

When it comes to crime, AI is a double-edged sword. On one hand, financial institutions are already using this tech’s incredible data-analyzing ability to identify fraudulent activity. AI is also helping to stop various other cybercrimes. But in the real world, policing should never be automated by AI. There’s too much potential for information misuse and violation of ethical boundaries.

Fake News

We are on the verge of having neural networks that can?create photo-realistic images?or replicate someone's voice in a pitch-perfect fashion. With that comes the potential for hugely disruptive social change, such as no longer being able to trust video or audio footage as genuine. Concerns are also starting to be raised about how such technologies will be used to misappropriate people's images, with tools already being created to splice famous faces into adult films convincingly.

Facial Recognition and Surveillance

In recent years, the accuracy of facial recognition systems has leapt forward, to the point where?Chinese tech giant Baidu says it can match faces with 99% accuracy, providing the face is clear enough on the video. While police forces in western countries have generally only trialed using facial-recognition systems at large events, in China, the authorities are mounting a nationwide program to connect CCTV across the country to facial recognition and to use AI systems to track suspects and suspicious behaviour, and has?also expanded the use of facial-recognition glasses by police. Although privacy regulations vary globally, it is likely this more intrusive use of AI technology - including AI that can recognize emotions - will gradually become more widespread.

AI, Privacy and Human Rights

AI’s reliance on big data is already impacting privacy in a major way. Look no further than Cambridge Analytica / Facebook scandal or Amazon Alexa?eavesdropping, two among many examples of tech gone wild. Without proper regulations and self-imposed limitations, critics argue, the situation will get even worse. In 2015, Apple CEO Tim Cook derided competitors Google and Facebook for greed-driven data mining. “They’re gobbling up everything they can learn about you and trying to monetize it,” he said in a?2015 speech. “We think that’s wrong.”

Last fall, during a talk in Brussels, Belgium, Cook expounded on his concern. “Advancing AI by collecting huge personal profiles is laziness, not efficiency," he said. “For artificial intelligence to be truly smart, it must respect human values, including privacy. If we get this wrong, the dangers are profound."

“If implemented responsibly, AI can benefit society. However, as is the case with most emerging technology, there is a real risk that commercial and state use has a detrimental impact on human rights"

Plenty of others agree. In a?paper?published recently by UK-based human rights and privacy groups Article 19 and Privacy International, anxiety about AI is reserved for its everyday functions rather than a cataclysmic shift like the advent of robot overlords. “If implemented responsibly, AI can benefit society,” the authors write. “However, as is the case with most emerging technology, there is a real risk that commercial and state use has a detrimental impact on human rights. In particular, applications of these technologies frequently rely on the generation, collection, processing, and sharing of large amounts of data, both about individual and collective behaviour. This data can be used to profile individuals and predict future behaviour. While some of these uses, like spam filters or suggested items for online shopping, may seem benign, others can have more serious repercussions and may even pose unprecedented threats to the right to privacy and the right to freedom of expression and information (‘freedom of expression’). The use of AI can also impact the exercise of a number of other rights, including the right to an effective remedy, the right to a fair trial, and the right to freedom from discrimination”.

"Good guys and bad guys both use AI, but the bad guys don't need to worry about complying with rules and regulations".

- SearchSecurity (2019)

The vast and far-reaching influence and impact that social media and digital ad revenues powered by AI / ML and its pervasive influence and outcomes that it is having in particular with political campaigns, elections, referendums (eg, UK Brexit vote outcome, US 2016 presidential election and Cambridge Analytica/Facebook scandal), and also with the proliferation of false information and negative influences and real threats such as the rise of violence, racism, terrorism, extremism societal political divide, surveillance, etc.? It is hard for me to express the scale and magnitude, the accelerating and dizzying rate of speed on the impact and detrimental costs to society, humanity and our everyday lives - it is staggering and mind-blowing!

There needs to be a greater sense of urgency, a great deal more needs to be done around AI regulation, transparency, accountability and standards for AI. Harsher penalties, civil and criminal charges including jail terms are urgently required for individuals, companies and company directors blatant breaches in violating privacy and data misuse (as well as AI regulations which do not exist today). For example, the Cambridge Analytica and Facebook scandal (2016) - one of the most notorious companies in violating privacy and data misuse, where AI played a key role in the efforts to target and influence US voters taking data from millions of Facebook profiles without user acknowledgment to create psychological profiles of voters. The result — public uproar, Facebook was fined US $5 billion and Cambridge Analytica went bankrupt. That is a slap on the wrist considering that Facebook is now valued at?US $1 Trillion and the detrimental impacts to US citizens and society caused by their breach, with no individuals/company directors being given jail terms. The individuals from Cambridge Analytica are free to start up another company under a different name to do it all over again - with nothing stopping them. Not to mention the implications on the influence this breach had on the 2016 US election and the Trump win and impact on future elections.

AI Value - for Society and Humanity

Amid the controversy that surrounds certain applications of AI, some groups are highlighting the good it can do. In order to realize the potential and value of AI, there are also some inherent risks that must be proactively considered, mitigated, managed and monitored by business, governments, society and wider humanity in order to realise its value and beware of the noise, hype, challenges, failures, pitfalls and potential threats that comes along with it.

Augmented Human Intelligence

The key to successful AI will be augmented intelligence, in which AI can empower humans by amplifying their skills to make better decisions. This will drive efficiency in so many areas in society and has the power to change things for the positive. The key is to remember that technology is here to assist us, not replace us.

Faster Healthcare Advancements

AI has been called on to speed up drug development through deep learning as well as to distill COVID-19 related information in the media and reduce the sharing of misinformation. AI has been called on to speed up drug development through deep learning. AI techniques from deep learning, image classification and object recognition can now be used to find cancer on MRIs with the same accuracy as highly trained radiologists.

Enhanced Cybersecurity

AI is very important to our future given the increase in cyberattacks and the reduced time to breach by cybercriminals. AI can go beyond what automated systems can do by learning as the threat landscape evolves. With the right training, over time AI is capable of hunting for, detecting and responding to incidents or breaches with little human intervention. This can save lives, resources and time.

More Accurate Natural Disaster Prediction

As climate change continues to affect the world, society faces more natural disasters, more often. AI is helping specialists across a variety of domains predict large-scale events that can dramatically impact people. While we cannot prepare for everything all the time, scientists are using AI-powered tools to better forecast future disasters, saving larger portions of people than they could before.

BOTTOM LINE

As AI becomes increasingly embedded deeper?into the fabric of business, industry, society, humanity and our everyday lives, here are some key findings and takeaways on the impacts, risks and value of Artificial Intelligence.

Key Findings and Takeaways for Business and Industry

AI Impacts

• Artificial Intelligence will change the way we work, live and interact.
• AI is bringing data and technology at the center of the company.
• Bias can often be introduced into AI by the data that is used to train machine learning models.
• When you use AI to support business-critical decisions you need to be sure that you understand what AI is doing, and why.
• Boards and executives need to learn more about how to become an industry leader in the responsible use of AI.
• The successful adoption of AI and other advanced technologies will require cooperation from multiple stakeholders, especially business leaders and the public sector.
• CEOs must embrace new, farsighted partnerships for social good. ?
• Given the potential for a win–win across business, industries and society from a socially careful and innovation-driven adoption strategy, the time has come for business leaders across sectors to embed a new imperative in their corporate strategy — technological social responsibility (TSR).

AI Risk

• Most companies are still not acknowledging most AI risks.
• With great power comes greater responsibility. Organizations can mitigate the risks of applying artificial intelligence and advanced analytics.
• Whilst companies overall are making some progress in mitigating the risks of AI, most still have a long way to go.
• AI models ruthlessly optimize what you tell them to. Done correctly this is a good thing. However, it’s a double-edged sword that can lead to serious ethical, market branding and legal issues. Nefarious actors can intentionally create such issues, but often these issues arise by lack of oversight or by accident.?
• Many organisations and data scientists still do not focus on the ethical conundrums that they might encounter. To help ensure ethics are considered during an AI / ML / data science project, executives and leaders should ask specific questions to identify and address data science ethical conundrums in their organisations.
• Responsible AI is the only way to mitigate AI risks.
• The proposed EU rules are just one more step toward global AI regulation.
• Smart organizations are preparing for compliance — and managing AI risk.
• A systematic approach to identifying and prioritizing AI risks can help organizations effectively target their risk mitigation efforts.
• Proactively adopt Responsible AI principals for their organisations, at some point they will become the global standard. AI and the machine learning models that support it should be comprehensive, explainable, ethical and efficient.
• Establish a robust Responsible AI policies and governance framework for the organisation that is actively followed, enforced, monitored and reported on.
• Ongoing scrutiny is crucial to ensure an organization is committed to providing an unbiased, trustworthy AI.
• The establishment of ethics boards at companies that rely on AI, whether as a service or a product.

AI Value

• COVID-19 pandemic has pushed companies over the digital transformation, automation and AI technology tipping point — and transformed business forever.
• AI can help?boost revenues?through increased personalization of services to customers (and employees);?lower costs?through efficiencies generated by higher automation, reduced errors rates, and better resource utilization; and?uncover new and previously unrealized opportunities?based on an improved ability to process and generate insights from vast troves of data.
• Even though most machine-learning algorithms are open source, it is possible to keep a technological edge thanks to fine-tuning of hyper-parameters for specific tasks and agile management of machine-learning pipelines to continuously incorporate technological improvement.
• Most companies underestimate the role of mutual learning between humans and machines in generating value from AI.
• There is a significant gap between AI high performers and others (the rest) on realizing and maximizing value from AI.
• Significant financial benefits with AI are noticeably amplified when organizations develop multiple effective ways for humans and AI to learn together.
• Business executives and boards need to understand that implementing AI is a capital-intensive technology and that it shows results in the long run. A company needs to set a long-term AI objective to ensure success.

Key Findings and Takeaways for Society and Humanity

AI Impacts

• Artificial Intelligence is going to change the world more than anything in the history of mankind.
• AI is moving from the lab to the workplace, to our everyday lives with profound impacts and implications for business, society and humanity.
• Governments, people, society and humanity have been primarily distracted with pandemic pandemonium, restrictions, lockdowns and COVID-19 vaccinations (for the past 18+ months), meanwhile the advances of AI adoption in business has accelerated and taken a quantum leap and so to has its impacts and risk to society and humanity.
• AI is influencing and impacting our choices in one way or another.
• If you are using social media, most of your decisions are being impacted by artificial intelligence.
• Companies and industries are making significant profits and revenue from AI / ML, that is projected to accelerate and exponentially increase (mostly profit-driven) with far-reaching impacts on society and our everyday lives.
• Artificial intelligence is affecting our decisions and our lifestyles every day. It is very pervasive, far-reaching and will only continue to proliferate, becoming even further embedded into our society and every aspect of our lives.
• If the AI / ML research pioneers and the tech world considers that "today’s artificial-intelligence systems are not actually intelligent", imagine the pervasiveness and impacts of more intelligent AI in the near-term and longer-term future.
• The entire tech world is debating the consequences of artificial intelligence and the part AI is going to play in shaping our future. This debate is something that government, customer advocacy, society and community leaders need to be leading, which needs to go beyond the tech world as it has far reaching implications for society and humanity.
• AI will redesign how our society works from the ground up.
• As nations vie for AI leadership, AI research output is increasing rapidly.
• Many countries see AI as providing a competitive edge, not only economically, but militarily.
• Anybody (individuals, corporations, countries) that has a mastery over AI and is investing in implementing it retains an economic lead.
• AI will be an accelerant for further income and economic inequality in our society. Jeff Bezos net worth in 2009: US $6,800,000,000 (US $6.8 Billion); Jeff Bezos net worth in 2021: US $191,300,000,000 (US $191.3 Billion). The US Federal minimum wage in 2009: $7.25; the US Federal minimum wage in 2021: $7.25.
• There is widespread misunderstanding among people and society on what AI is and its potential - most think that AI is a product or technology, but it is actually an enabler for almost everything we do.

AI Risks

• The power and potential danger of AI in the wrong hands.
• The vast and far-reaching influence and impact that social media and digital ad revenues powered by AI and its pervasive influence and outcomes that it is having in particular with political campaigns, elections, referendums (eg, UK Brexit vote outcome, US 2016 presidential election and Cambridge Analytica/Facebook scandal).
• AI’s power to help create or spread false information. The proliferation of false information and negative influences and threats such as the rise of racism, terrorism, extremism, societal political divide, etc. and the scale and magnitude of the detrimental impact and costs to society and humanity and our everyday lives and decision-making.
• Governments, customer advocacy groups, society and community leaders must provide greater citizen and consumer awareness and education about the impacts of AI into our society and everyday lives. Most are completely oblivious and unaware of some of the powerful impacts, pervasiveness and influence that AI already has in our everyday lives.
• Governments are moving at a glacial pace with regards to AI regulations and are subject to being heavily influenced by business and industry.
• There exists many multiple competing conflicts of interests and vested interests around the role of AI specifically by governments and politicians across the globe (irrespective of party, whether far-left, left, center, right or far-right) in election campaigns funding/elections/re-elections and referendums being heavily funded and influenced by business and industry, not to mention the many hidden personal agendas and self-interests in profiting from AI by politicians.
• People and society must take a lead role to speak truth to power to persuade governments to take action around regulation of AI to protect its citizens and society.
• The race to the top among the world’s leaders in artificial intelligence - as revenues and research output soar in the field of AI.
• AI algorithms that ingest real-world data and preferences as inputs may run a risk of learning and imitating possible biases and prejudices.
• Biases built into its algorithms could result in ethical and human rights abuses. with negative real-world consequences.?
• There are currently no global regulations or standards adopted across AI, in particular for prevention and accountability when AI programming creates intended or unintended consequences.
• Spectacular AI failures provide examples of companies violating privacy and data misuse.
• More intrusive use of AI technology, such as facial recognition and surveillance — including AI that can recognize emotions — will gradually become more widespread.
• As AI will continue to proliferate at an accelerating pace, there is an urgent need for AI regulations and global standards to be established to protect citizens, consumers, society and humanity.
• Responsible AI needs to become a leading priority among organisations and industry leaders for AI applications, with emphasis on improving privacy, explainability, bias detection and governance.
• For most organisations, AI ethical risks are not a top-of-mind information technology concern.
• Substantially increased amount of venture capital pouring into the AI-driven biotech startups and pharmaceutical industry (around $1.9B in 2020 alone, so far), as well as AI deals and alliances during the pandemic which is alarming.
• Concerns over AI's effect on privacy and truth is key.?
• The rise of financial robotization and use of artificial intelligence is not only changing the speed and makeup of the stockmarket. It raises some alarming and concerning questions about the function of markets, the impact of markets on the wider economy, how companies are governed and financial stability. This is already having a monumental detrimental impact to society, humanity and the global economy, leading to significant widening of the income inequality gap, the redistribution of wealth from society to benefiting mostly corporations and the relatively few wealthy individuals. With no rules and regulations around AI, the use of AI in financial markets AND the lack of adequate financial regulation, controls and transparency around the financial stock markets AND quant / passive funds using AI-powered algorithms — this will only continue its hyper-ascending meteoric rise at an increasingly alarming pace to the massive detriment of society and humanity, which may eventually lead to a significant economic collapse if continued to be left untethered.
• Harsher penalties, criminal charges including jail terms are required for blatant breaches in violating privacy and data misuse. For example, the Cambridge Analytica scandal (2016) violating privacy and data misuse. The result — public uproar, Facebook fined $5 billion and Cambridge Analytica went bankrupt. That is a slap on the wrist considering that Facebook is valued at?US $1 Trillion and the detrimental impacts to US citizens and society caused by their breach.
• AI can help society as much as business and industry — but only if leaders start embracing technological social responsibility as a new business imperative.
• Making the responsible use of AI a government and societal concern a priority and accelerate the regulation of it with adequate scrutiny, transparency, accountability and penalties for responsible individuals and company directors for any violations and breaches.

AI Value

• The key to successful AI will be augmented intelligence, in which AI can empower humans by amplifying their skills to make better decisions. The key is to remember that technology is here to assist us, not replace us.
• From crucial life-saving medical gear to self-driving vehicles, AI is going to be infused to virtually every program and apparatus.
• The use of artificial intelligence has given a new dimension to healthcare with faster healthcare advancements.
• Enhanced cybersecurity, AI can go beyond what automated systems can do by learning as the threat landscape evolves. With the right training, over time AI is capable of hunting for, detecting and responding to incidents or breaches with little human intervention. This can save lives, resources and time.
• AI is helping scientists and specialists predict large-scale events that can dramatically impact people using AI-powered tools to better forecast future disasters, saving larger portions of people than they could before.
• There are profound impacts and value that can be realized from AI for business, industry, society and humanity, if and when AI is used in a responsible way (an unbiased and trustworthy AI). Along with the value however, comes risk. A far more effective and balanced approach is required to mitigate the risks and realize the value for society and humanity (not just value for corporations, industry, politicians and the relative few powerful and wealthy individuals at the top).

A Lasting Thought

"The single most critical driver of [impacts, risks and] value from AI is not algorithms, or technology — it is the human in the equation".

- Shervin Khodabandeh, Senior Partner and Managing Director, BCG

SOURCES:

BCG (2020) - www.bcg.com/en-au/press/20october2020-study-finds-significant-financial-benefits-with-ai

Bloomberg (2021) - www.bloomberg.com/professional/blog/passive-likely-overtakes-active-by-2026-earlier-if-bear-market/

Data Science Process Alliance (2021) - www.datascience-pm.com/10-data-science-ethics-questions

Deloitte (2020) - www2.deloitte.com/content/dam/insights/us/articles/4780_State-of-AI-in-the-enterprise/DI_State-of-AI-in-the-enterprise-2nd-ed

Forbes (2020) - www.forbes.com/sites/forbestechcouncil/2020/06/26/15-tech-experts-share-potential-impacts-of-ai-on-society/?sh=1177c8ae3714

Gartner (2021) - blogs.gartner.com/andrew_white/2021/01/12/our-top-data-and-analytics-predicts-for-2021

McKinsey (2020) - www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/global-survey-the-state-of-ai-in-2020

PWC (2021) - www.pwc.com/gx/en/issues/data-and-analytics/artificial-intelligence/what-is-responsible-ai