Article originally published on LINKtoLEADERS under the Portuguese title “Sem saber ler nem escrever!”
In the 80s, “with no knowledge, only intuition”, I discovered the world of computing. I believed computers could do everything, as if it were an electronic God. But when I asked the TIMEX Sinclair 1000 to draw the planet Saturn — I am fascinated by this planet, maybe because it has rings —, I only glimpse a strange message on the black and white TV:
Right after the Big Bang, in the Planck epoch, the Universe occupied a space region with a radius of 1.4 × 10-13 cm – remarkably, equal to the fundamental length characterizing elementary particles. Analogue to the way nearly all cells contain the DNA information required to build the entire organism, every region the size of an elementary particle had then the energy necessary for the Universe’s creation.
As the Universe cooled down, electrons and quarks were the first to appear, the latter forming protons and neutrons, combining into nuclei in a mere matter of minutes. During its expansion, processes started happening slower and slower: it took 380,000 years for electrons to start orbiting around the nuclei, and 100 million years for hydrogen and helium to form the first stars. Even more, it wasn’t until 4.5 billion years ago that our young Earth was born, with its oceans emerging shortly after, and the first microbes to call them home for the first time. Life took over our planet in what seems, on the scale of the Universe, a sheer instant, and turned this world into its playground. There came butterflies and tricked the non-existence of natural blue pigment by creating Christmas tree-shaped nanometric structures in their wings to reflect blue’s wavelength only; fireflies and lanternfish which use the chemical reaction between oxygen and luciferin for bioluminescence; and it all goes all the way up to the butterfly effect leading to the unpredictability of the weather forecasts, commonly known as the reason why a pair of wings flapping in Brazil can lead to a typhoon in Texas. The world as we know it now developed slowly, and with the help of continuous evolution and natural selection, the first humans came to life.
Without any doubt, we are the earthly species never ceasing to surprise. We developed rationality, logic, strategic and critical thinking, yet human nature cannot be essentially defined without bringing into the equation our remarkable appetite for art and beauty. In the intricate puzzle human existence represents, this particular piece has given it valences no other known being possesses. Not all beauty is art, but many artworks both in the past, as well as today, embody some understanding of beauty.
To define is to limit, as Oscar Wilde stated, and even though we cannot establish clear definitions of art and beauty. Yet, great works of art manage to establish a strong thread between the creator and receptor. In contrast to this byproduct of human self-expression that encapsulates unique creative behaviour, beauty has existed long before our emergence as a species and isn’t bound to it in any way. It is omnipresent, a metaphorical Higgs field that can be observed by the ones who wish to open their eyes thoroughly. From the formation of Earth’s oceans and butterflies’ blue wings to Euler’s identity and rococo architecture, beauty is a subjective ubiquity. Though a question remains – why does it evoke such pleasure in our minds? What happens in our brains when we see something beautiful? The question is the subject of an entire field, named neuroaesthetics, which identified an intricate whole-brain response to artistic stimuli. As such, our puzzling reactions to art can be explained by these responses similar to “mind wandering”, involving “thoughts about the self, memory, and future”– in other words, art seems to evoke our past experiences, present conscious self, and imagination about the future. There needs to be noted that critics of the field draw attention to the superficiality and oversimplification that may characterize our attempts to view art through the lenses of neuroscience.
We’re at a fascinating point in the discourse around artificial intelligence (AI) and all things “smart”. At one level, we may be reaching “peak hype”, with breathless claims and counter claims about potential society impacts of disruptive technologies. Everywhere we look, there’s earnest discussion of AI and its exponentially advancing sisters – blockchain, sensors, the Internet of Things (IoT), big data, cloud computing, 3D / 4D printing, and hyperconnectivity. At another level, for many, it is worrying to hear politicians and business leaders talking with confidence about the transformative potential and societal benefits of these technologies in application ranging from smart homes and cities to intelligent energy and transport infrastructures.
Why the concern? Well, these same leaders seem helpless to deal with any kind of adverse weather incident, ground 70,000 passengers worldwide with no communication because someone flicked the wrong switch, and rush between Brexit crisis meetings while pretending they have a coherent strategy. Hence, there’s growing concern that we’ll see genuine stupidity in the choices made about how we deploy ever more powerful smart technologies across our infrastructure for society’s benefit. So, what intelligent choices could ensure that intelligent tools genuinely serve humanity’s best future interests.
Firstly, we are becoming a society of connected things with appalling connectivity. Literally every street lamp, road sign, car component, object we own, and item of clothing we wear could be carrying a sensor in the next five to ten years. With a trillion plus connected objects throwing off a continuous stream of information – we are talking about a shift from big to humungous data. The challenge is how we’ll transport that information? For Britain to realise its smart nation goals and attract the industries of tomorrow in the post-Brexit world, it seems imperative that we have broadband speeds that puts us amongst the five fastest nations on the planet. This doesn’t appear to be part of the current plan.
What are new practice areas that solo, small, and medium firms should prepare for in their 5 to 10-year plans for the future?
In the search for the next wave of growth, future-focused law firms are learning to embrace the futurist perspective as they evaluate the opportunities arising from cutting-edge technologies such as artificial intelligence (AI). These technologies will enable new organizational structures, services, and business models in the business horizon. Here are three new practice areas that firms might want to prepare for in the coming few years.
1. Evidence and liability issues from autonomous machine “testimony”
A growing array of “smart” objects are enveloping our homes, workplaces, and communities and the volume of legally admissible data from these devices is likely grow at an exponential rate over the next decade. Firms need to start building expertise around the admissibility and verifiability of the data collected. For example, the design trend for voice-activated technology is driving a rash of seemingly sentient technology in the form of digital assistants, smart appliances, and personal medical and wearable devices. Law firms may be asked to represent clients in cases dealing with evidence, witnesses, accidents, or contracts hinging on theoretically immutable digital proof such as time-stamped video and audio recordings. Attorneys may seek to specialize in addressing the data issues related to domains such as digital twins and personas, surveillance capitalism (companies exploiting customer data for commercial gain with and without full approval), and digital privacy rights.
Life in the digital age is raising fundamental questions about the future of business and employment and hence the strategies, skills, and abilities we need to develop to survive in the next economy. This article explores two key changes that we need to start developing a core of capabilities for – namely the quest for exponential growth and the growing use of corporate venturing.
Why are these becoming important? Well, technology and the thinking it enables are driving new ideas and experiments on commercial strategies, the shape and structure of organisations, business models, and the relationship with extended ecosystems of partners. Both strategies are seen as options to drive growth and accelerate the realisation of market opportunities.
Exponential thinking is seen as a fast track approach to driving business innovation and growth. We are used to the idea of exponential growth in many fields of science and technology. For example, Moore’s Law in information technology tells us that the amount of computer power we can buy for £1,000 doubles every 18–24 months. This has inspired digital innovators to try and grow their business at the same pace or faster than the underlying technologies. The broader business world is taking notice. The stellar rates of development and growth we are witnessing for some exponential businesses in the digital domain are encouraging many organisations across literally every sector from banking to aviation to try and apply similar thinking to some or all of their activities.
Hence, it is now common to see businesses pursue a vision of doubling of revenues within three to four years and a achieving a 2-20X or more improvement in other aspects of the business. For purely digital entities, their business models are predicated on using network effects to drive exponential growth or better in user numbers and revenues. Some suggest that to embrace the exponential model, businesses must reject defined end goals and step-by-step plans in favour of such ambitious visions and develop a high tolerance of uncertainty. Typically, the exponential growth initiatives are driven through a combination of iterative task specific ‘sprints’ to define, test, refine, and deliver business changes that could result in massive performance improvements in specific areas of the business.
How might the application of artificial intelligence enhance the experience and reach of electronic gaming and gambling?
Over the next few years, the internet gaming business could be transformed completely as artificial intelligence (AI) enters the scene. At its core, AI is a type software or hardware that learns—and it could be programmed to learn mostly about us, its users and those insights could drive the developments of new, hyper-personalised gaming and internet betting experiences. The technology is being applied to learn our habits, our likes, and our relationship patterns. Just as Netflix uses an algorithm to suggest films you might watch, the concept of personalisation is extending to the idea of “Lifestyle AI” applications that could help choose your entertainment, gaming choices, wardrobe, your next meal, your job, and romantic partner. Take this one natural step further, and we enter the domain of mass tailoring of gaming and betting experiences.
While it all sounds a bit like science fiction, the capabilities of AI tools and the range of applications are growing exponentially. Indeed, by 2020 AI could be present in some form in everything we do, and by 2030, AI is likely to have infiltrated our lives in much the same way as smartphones, the internet, and global travel are now taken for granted. So how might AI change our recreational habits and day-to-day existence in a way that might affect e-gaming? Here are eight novel ways internet betting could be different in future as a result of AI.
Trend Betting – Individuals could bet on the word, phrase, issue, or concept that will be mentioned most across a range of sites on the web during a fixed period, and then AI web crawlers would determine the actual count. Machine learning would be used to profile these trends and patterns over time, predict the likelihood and frequency of occurrence of key terms, and then determine the odds accordingly. Users could volunteer their own terms alongside those which the gambling sites suggest. To determine the initial odds for new terms, machine learning would compare the new term to others it has already analysed, and search the internet to see how frequently it is mentioned. The algorithm would then set the initial odds and refine them over time in response to actual betting patterns and payouts.
Campaign Betting – Companies could hedge the costs of their marketing campaigns by betting on their success. Machine learning algorithms could evaluate a campaign, compare keywords and phrases in the material against past campaigns, and then determine the odds accordingly. The company placing the wager could then bet on achieving or not achieving a certain target number of hits.
Next Generation Sports Betting – A combination of wearables and implantables tracking vital signs could be worn by sportspeople. Bets could then be placed on the aggregate performance of a team in a game—average heart rate, total calorie consumption, median oxygen intake, etc. The AI system would crunch the numbers in real time and generate minute by minute predictions of the likely outcomes for the rest of the event. Gamblers would be able to jump in at any time to bet on the likely outcome. The odds would be generated by applying machine learning algorithms to analyse the vast amounts of data generated from previous games.
Betting on Your Life – With AI, any scenario could turn into a betting opportunity. What are the chances that you would run into a friend at the grocery store? Find a lucky penny? Get a call from your parents? Enjoy your date? Go and see a movie? Be fired by your boss tomorrow? In a form of crowdsourced betting system, if you find enough people to bet on your life events then you could give it a go. Even individuals’ lives could be ranked according to their predictability or spontaneity. The algorithm would do a detailed comparison of your social media profiles and other web postings and data against its databank to determine the odds and change them dynamically as the bets roll in.
Beat the Bookies – With the analytical capability of AI, an independently developed ‘Beat the Bookie’ app could look at all the variables associated with a sports event. The app might factor in player performance statistics, player behaviour information, weather, previous fixtures, key match events, and create a ‘best bet’ opportunity for the gambler from across all available betting sites. An interesting question arises over how long it would be before the bookmakers develop a counter to the app or a more sophisticated basis for gambling.
