Blockchain-Enabled Convergence

Humans often use the past as a guide for the future; this mistake often makes it impossible to adapt to our rapidly changing world. Technological progress is not linear, it’s exponential in nature, making it much harder to grasp. This means we constantly underestimate the pace of change and as software eats more industries, improvements compound as traditionally human-centric industries like healthcare, logistics and agriculture digitise. As these industries come online and capture, process and automate data; ownership of this data will define the state, market and nation over the next half a century. Blockchains are therefore one of the most significant technological innovations since The Internet and fundamental to Web 3.0. Blockchains, distributed ledgers, smart contracts and other decentralisation innovations provide the foundation for a scalable and secure data and asset management layer for the new Web 3.0. It acts as a platform to support individual rights while benefiting from the aggregation of vast amounts of data from the Internet of Things. They also ensure the benefits of artificial intelligence are shared broadly across society and do not aggregate to a few AI owners, or the 0.00001% of the population. The Internet of Things, artificial intelligence, autonomous robotics, 3D printing, as well as virtual & augmented reality

Blockchains are therefore one of the most significant technological innovations since The Internet and fundamental to Web 3.0. Blockchains, distributed ledgers, smart contracts and other decentralisation innovations provide the foundation for a scalable and secure data and asset management layer for the new Web 3.0. It acts as a platform to support individual rights while benefiting from the aggregation of vast amounts of data from the Internet of Things. They also ensure the benefits of artificial intelligence are shared broadly across society and do not aggregate to a few AI owners, or the 0.00001% of the population. The Internet of Things, artificial intelligence, autonomous robotics, 3D printing, as well as virtual & augmented reality are all converging in new and exciting ways. Blockchains will become the decentralised data and asset management layer that links the data and value from these technologies, ushering in the era of blockchain-enabled convergence.

Convergence is not a process that will happen immediately, nor be a simple and linear progression. Trends will combine at different speeds based on technical limitations, political and social barriers, as well as commercial considerations. The market dynamics will vary with industrial manufacturers and telecommunications providers leading the charge in the Internet of Things, while consumer Internet companies like Google and Facebook innovate in artificial intelligence. It is important to grasp the nuances of each market, but in doing so, it’s easy to miss broader macro-trends. The development of blockchains is a good example, as exceptionally talented developers push the boundaries of cryptography with zero-knowledge proofs and smart contracts but fail to see the implications on broader governance structures and political philosophies. These are the kind of things we have been trying to figure out since the dawn of civilisation. It is just as important in technological progress to study Plato and Hume as it is to study Von Neumann and Shannon.

As the rate of change increases, it is critical to understand these technology trends as part of a wider collective rather than as separate developments. Blockchain-enabled convergence is our attempt to capture this wide collective. The first part of the white paper explores blockchains, artificial intelligence, the Internet of Things, autonomous robotics, 3D printing, and virtual & augmented reality to understand the drivers and barriers to adoption. Part two investigates how blockchain-enabled convergence changes the trade value chain from manufacturing and design through logistics and distribution to retail and commerce, and even more profoundly changing the very governance structure of the organisation.

5 Key Themes

The white paper explores an extremely broad range of technologies and markets, yet despite this breadth, we found 5 key themes that kept coming up time and time again. These themes are not technological in nature but rather trends that will reshape markets, society and excitingly, the relationship between humans and machines.

1. Web 3.0 — The Global Trust Network

Web 3.0 underpinned by blockchains and decentralized technologies provide global trust. The core design of The Internet was enable the sharing of information. The core design of Bitcoin, and other open permissionless blockchains, is a network of trust for exchanging value and asset ownership. Web 3.0 provides trust and chain-of-ownership; the missing link with the existing Internet infrastructure.

2. The ‘Real’ Sharing Economy

New digital intermediaries have sprung up so individuals can ‘share’ unproductive assets; spare rooms on Airbnb, spare seats on Uber and spare time on TaskRabbit. These ‘sharing economy’ companies are nothing more than a new middleman sitting between a buyer and seller capturing outsized value. Blockchain-enabled convergence allows seamless peer-to-peer exchange of assets and value reducing the need for trust brokers in the middle of a market extracting economic rent.

3. The Killer Business Model: The Decentralized Data Marketplace

A blockchain-based data marketplace helps solve two major problems in artificial intelligence today, the access to data for those that need it, and monetizing unused data for those that have it. A decentralized data marketplace creates an economic mechanism for individuals and organisations to buy and sell data, reducing the incentive to hoard valuable unused data and remunerating the creators of data not just the processors.

4. The Commoditization of Logistics & Production

Blockchain-enabled convergence transforms the trade value chain. Autonomous robotics, AI, IoT and blockchains will digitise logistics and distribution reducing its importance and therefore ability for companies at this point in the value chain to capture profit. Producers can capture more of the value they create and consumers can pay less. In the long-term, technical deflation will hit the knee of the exponential curve as much of production gets commoditized by 3D printing, and virtual and augmented reality make it cheap to design and print products at home.

5. The Rise of the Decentralised Organisation

The global multinational corporation that developed to coordinate global trade is under threat as the dominant form of governing structure. New decentralised processes for business financing with Initial Coin Offerings (ICOs), incorporation, voting, payments and talent and project coordination are enabling start-ups to choose processes that are suitable for smaller, more agile start-ups rather than using an expensive corporate structure designed for large companies.

Special Thanks to Anish Mohammed, Trent McConaghy, @edcafenet, Vijay Michalik, Creative Barcode, @API_economics, David J Klein, and Ethan Gilmore of VarCrypt for conversations and contributions.

Blockchain-Enabled Convergence

Ford and the Innovators Dilemma

Auto-maker to mobility provider

‘It’s not for others to disrupt us, it’s for us to disrupt ourselves.” This was the theme of Ford CEO Mark Fields at the Detroit motor show last week. Disruption is the watchword in the auto industry as the tech industry begins seriously encroach on the auto industry.

Change is coming from multiple directions, not all of which fit the term ‘disruptive’ as defined by Clayton Christensen. The change in drivetrain from a combustion engine to electric led by Tesla is often described as disruptive but, in reality, is a change in the way cars a made. Moving to electric vehicles may shift some revenues around the existing market, but it does not represent a new business model that fundamentally reshapes the market. Ridesharing led by Uber, Lyft and Didi Kuaidi represent a shift from car ownership to transportation-as-a-service. Users pay for access to transportation rather than for ownership of a vehicle. This truly is a disruptive innovation as it will disrupt the existing automotive value chain. Increasing value and profits will be captured by software and application providers rather than hardware.

It is against this backdrop that Ford is attempting to position itself as a mobility company rather than simply an auto-maker. Ford is one of the first true disruptors. The Ford Model T in 1908 was the first mass produced automobile and ushered in the age of the affordable car. The question is; more than 100 years later, can the company disrupt itself rather than the market.

The strategy is Ford Smart Mobility, a programme that will drive Ford’s business in new areas like connectivity, data analytics, autonomous driving and customer experience. These are all services that will see Ford sell software and services to try to get closer to the customer. Rather than just provide the car and allow service providers have the relationship with the customer, Ford wants to make sure it captured value and profit as the disruption occurs.

Identifying the changing landscape is the easy part. The real challenge for Ford is two-fold, one is culture and the other is sustaining shareholder value. On the culture front, software and services require a very different approach than hardware. Designing and building cars is a long process and Ford has become very good at it. People who are good at it have been hired and culture has been embedded that helps the company build better and better cars. But building a car requires a very different skillset and culture than delivering data analytics or connectivity for the driver. Data scientists need to be hired, user design needs to be a focus, and A/B testing needs to be infused across the company. In hardware companies, people good at hardware are highly valued and hold positions of power within the company. This means people with incompatible skill-sets and approaches to business hold powerful positions within the company and are reluctant to change what has made them and the company successful in the past.

Culture is difficult to change, but sustaining shareholder value can be more pernicious. The Innovators Dilemma is so difficult to address because executives have to make business decisions that go against the financial interests of the company. The fact is, as Ford tries to become a mobility company this takes resources away from their core money-making business. The return on investment for mobility services is lower and higher-risk than the auto part of the business.

Ford need to balance the legacy business model of selling cars with the growth business of selling services. Uber, on the other hand, is a technology company that has hired people and built a culture that values software ahead of everything else. The core business is providing transportation-as-a-service and providing the best possible customer experience. They can focus all of their resources on doing this and this alone. The whole company is pulling is the same direction making execution faster and easier.

Ford may well manage to change the culture and communicate its vision successfully to shareholders. In order to truly compete, they need to split the company. They need to be clear about exactly what disrupting yourself means:

Disruption means generating less revenue in the short term. When put in those terms disrupting yourself doesn’t sound quite so innovative. This is exactly what the Innovators Dilemma is so difficult to solve.

No one can serve two masters

Ford should split up the newly created mobility entity and auto entity into two separate businesses. The mobility entity needs to be free to pursue strategies that may negatively impact the core business. This is much harder inside a single entity. This business should be given the freedom to take risks and given more autonomy to make decisions. The mobility business can leverage the resources, infrastructure and institutional knowledge to serve customers, but not to be beholden to the core business.

Such an entity would have access to a huge installed base of cars generating a tremendous amount of data on the roads every day. This is real-time road and driving data Google needs. This data will give the company a major advantage in the machine learning space as discussed in my recent post The Only Thing That Matters in Machine Learning is… The two business approach gives Ford a fighting chance in the transportation-as-a-service market.

Ford and the Innovators Dilemma

Apple and Artificial Intelligence: The Odd Couple

Apple Builds Premium Computers as the World Moves toward Commoditized Invisible Computing

Apple is doomed. I said it. Sell your stock now, get out while you can.

Well, that isn’t quite true, but I do think that as we move into the next computing paradigm, the Cognitive Era, Apple’s business model and culture will need to change. The Cognitive Era describes the next phase in technology development in which the world around us The future success of Apple will not be determined by the iPhone, iPad, Watch, or even a car. For Apple to continue its dominance of the technology market and expand into healthcare, transportation, and fashion, it needs to move away from being a computer company. This may sound like a ridiculous statement considering Apple’s position at the moment. But as the Internet of Things gathers pace with every device connecting to the Internet, the value is in making these devices do smart things. Similar to electricity today, computing power will be on tap whenever and wherever we need it. The next computing paradigm, the Cognitive Era, with ubiquitous and invisible computing power poses an existential threat to Apple.Apple is a company that makes money from people who are happy to pay extra for a computer (phone, tablet, or watch). The long-term danger to Apple’s business is that AI solves problems such as natural language understanding, computer vision, and behaviour prediction.

Apple is a company that makes money from people who are happy to pay extra for a computer (phone, tablet, or watch). The long-term danger to Apple’s business is that AI solves problems such as natural language understanding, computer vision, and behaviour prediction.

Artificial intelligence becomes the only product differentiator

Right now, Siri, Google Now, and Cortana are similar enough that the user might be unable to tell the difference in quality. Continued progress in combining deep learning with other machine learning techniques such as genetic algorithms and bayesian inference, together with data network effects, means the quality of applications will begin to diverge. Smartphones, wearables, and cars will be bought based on what they can do rather than how they look. Sure, design will always matter and there will always be a high end of the market, it’s just this will matter less for the products Apple sells. Apple needs to become an AI company.

Apple’s only true competitor in the smartphone space is Google, and at its core, Google is an AI company. Google’s guiding strategy is to do whatever is necessary to collect data to feed into its AI engine. Google Fiber, Loon, and Chrome are all products designed to get more people using the Internet and to leave behind more data. Nest, Waze, and Dropcam are services that generate vast amounts of data. Moreover, Google has the best AI experts on the payroll. Geoffrey Hinton and his team are pioneers of deep learning and have invented many of the most widely used tools. DeepMind, a 2014 acquisition, was recently on the cover of Nature magazine and is leading the progress in general AI. When it comes to data and talent, Google is miles ahead of Apple.

Apple is Waking up to the Threat of AI

In the last 6 months, Apple has attempted to address the AI threat by acquiring 3 leading machine learning companies: VocalIQ, Emotient, and Perceptio. VocalIQ is a UK-based machine learning company that builds voice user interfaces. Perceptio is a US-based deep learning company focusing on developing smartphone-based computer vision solutions. Perceptio’s solution is unique in that unlike most computer vision products, it allows smartphones to identify images independently without requiring access to cloud-based data libraries. The most recent acquisition was Emotient which is attempting to automate facial recognition and analysis. Their vision is to build emotionally aware technologies.

The technology behind VocalIQ will be useful for the Siri and the Apple TV teams. Emotient and Perceptio will help bring local intelligence to FaceTime, photos, and Apple TV. The local angle is important firstly because Apple’s core business is in devices not cloud like Google. The more processing that can be done on the device the better for Apple. Regardless of how the acquisitions will fit existing products, the fact that 3 AI companies with deep learning expertise have been acquired in such a short space of time shows Apple’s commitment to bolster its AI capabilities.

Apple’s Secrecy and Closed Approach Will Limit Its Ability to Succeed in AI

What made Apple so successful in the mobile era was its fully integrated approach to building computers. By vertically integrating the supply chain and building the hardware and software, Apple has offered an unparalleled user experience that enabled the company to maintain premium prices and high margins in an exceptionally competitive market. This vertical integration required tight control across the value chain to ensure the products and user experience are market leading. This focus on providing the best user experience created a relatively closed and secretive company culture.

This culture means Apple has not embraced the open-source community in the same way as Google, Facebook, and other leading AI companies. For example, the 2015 Neural Information Processing Systems (NIPS) conference was the largest annual gathering of deep learning experts. In addition to Google and Facebook, Baidu and Microsoft were present; however, Apple was not. Over the last year, as detailed in The Only Thing That Matters in Machine Learning is Data, Google, Microsoft, IBM, and Facebook have released their machine learning frameworks for free to the open-source community because in the machine learning field, only data and developers matter. This strategy collects more data and obtains more developers and PhDs using their tools to make it easier to recruit them in the future.

Apple is still to publish a research paper on AI, despite using the technology and that Siri is a leading natural language processor. The secrecy runs counter to the prevailing approach within the AI and, specifically, the deep learning community. The small talent pool is attracted to the open approach of Google and Facebook and even the new venture OpenAI that plans to release all research to the community. In fact, the speed of progress in the field is due in large part to the collaborative approach of researchers who have worked together in small laboratories and have taken their academic approach to the companies who hired them. Apple’s secrecy and closed approach directly impacts its ability to attract the best deep learning talent.

Can Apple Adapt to the AI Era?

The integrated approach that made Apple successful in the mobile era could be the very thing that makes it unsuccessful in the cognitive era. Apple is one of the few companies to have successfully navigated a paradigm shift in computing, from PC to mobile. That shift, however, favoured Apple’s focus on user experience and, therefore, a closed and controlled culture. In the PC era, the main buyers of PCs were heads of IT departments who decided what to buy based on quantifiable indicators such as processor speeds, RAM, and compatible software. Apple’s focus on the user interface is less tangible; therefore, its proposition was weaker. In the mobile era, however, consumers are the buyers of devices that are used pretty much all day every day. User experience is far more important. Apple has the right company culture to build consumer devices.

The shift from the mobile era to the AI era does not suit a closed company culture. In the field of AI, an open and collaborative culture is needed. Without changing the culture, Apple’s products will be weaker than its competitors.

AI is the biggest threat to Apple’s position as the most valuable company in the world.

Apple and Artificial Intelligence: The Odd Couple

OpenAI — Saving the World or Itself?

In AI, only data matters and a few companies are grabbing it all

In ‘The Only Thing That Matters in Machine Learning is Data’, I argued that the most important asset today is data. Companies that collect the most data win. More data means more accurate systems, means better products, means more customers, means more data. This virtuous cycle is why no other company can catch up with Google in search or Facebook in social. This was a nightmare for Bing and MySpace, but not a problem for Toyota or BP. They went about their business as usual.

In the olden days (90s/00s), market boundaries were clear and competitors pretty much came from the same market. What Internet companies were doing on the Internet was a curiosity. But now Google has Project Loon, Nest, a life science division, autonomous vehicles, and scary robots. Facebook Oculus, Instagram, WhatsApp and Messenger, and ambitions in the payments. Microsoft is looking to solve machine translation in Skype. Baidu is aiming to introduce autonomous buses within the next three years. The vision and scale of these companies are like nothing the business world has ever seen.

It is now dawning on most businesses that transformation is likely to come from outside traditional markets. Real transformation in healthcare is not going to come from Siemens or Medtronic. In the transport industry, it is not going to come from Toyota or Volkswagen. Companies with machine learning talent and mountains of data will swan in with products that incumbents can’t match. It is easy to copy new car designs and smaller ultrasound devices. Much less so when it comes to personalised healthcare treatments and autonomous vehicles.

Good luck trying to copy those without data.

Natural language processing software works on data pretty much regardless of the source of said that – smartphone, games console, home appliance. Data is data and not constrained by the industry it comes from. Computer vision will analyse video, it doesn’t matter if it is video from broadcasts, Periscope, or security cameras. Expertise in machine learning and deep learning is applicable across every industry. Google, Facebook, Microsoft, Baidu, and IBM can apply their technologies across all industries. The virtuous cycle of data means it will likely play out as a winner-takes-virtually-all in each market.

OpenAI aims to distribute AI to everybody

So where does OpenAI fit in? OpenAI is a counterweight to the monopolisation of data and power that is likely to play out across industries. The company’s founders are Ilya Sutskever, a deep learning expert, and Greg Brockman, former CTO of Stripe. The company’s backers include Elon Musk, Sam Altman, Peter Thiel, and Reid Hoffman. The company has over $1 billion in commitments and as a not for profit, it is planning on giving away all research and patents. Note — research and patents, not data.

OpenAI has some serious talent and money at its disposal. The vision is to use this talent and money to distribute AI gains rather than letting it accumulate to a few powerful companies. There is no such thing as altruism in business. OpenAI is no exception. It will bring tremendous benefits to Musk, Altman, and other investors. Simply put, the company is a giant repository for data. Tesla and SpaceX can pool their data with Y Combinator companies such as Airbnb, Dropbox, and Stripe. OpenAI can offer huge data sets for engineers, equal to what Google or Facebook can offer, with the added bonus that OpenAI has a noble cause.

Investors understand that they cannot compete with Google or Facebook for data or talent. OpenAI is their play. By giving away patents and research, OpenAI will not be doing anything Facebook and Google are not already doing. Google has given away TensorFlow. Facebook has open-sourced its deep learning software for Torch and Big Sur, it’s deep learning hardware design.

To truly benefit humanity, OpenAI should share its data. Without sharing the data, OpenAI is like a chef preparing a coq au vin but not sharing the recipe. The coq au vin tastes delicious, sure, but without the ingredients, making one at home will result in garlicky overcooked chicken. Nobody wants garlicky overcooked chicken.

OpenAI — Saving the World or Itself?

The Only Thing That Matters in Machine Learning is…

The hot trend in machine learning is giving away stuff for free. Tech companies have always been advocates of the open-source community and are happy to release parts of their code as open-source. Over the last year, however, the big players in machine learning have given away complete codebases. Google made its TensorFlow open source and Facebook gave away its optimised deep learning modules for Torch, another open-source library. Then, Microsoft released its Distributed Machine Learning Toolkit (DMTK) for free and, not to be outdone, IBM open-sourced its SystemML platform.

These developments have explicitly confirmed what observers already know; tech companies no longer see software and algorithms as valuable assets to keep proprietary. The most valuable asset, today, is data. The second most valuable asset is the talent to use this data.

2015, the year of open source

Facebook — Deep learning modules for Torch

In January, Facebook was the first to open-source its machine learning code. Facebook’s artificial intelligence (AI) efforts are run out of its AI research lab known as FAIR. In the lab, Facebook uses Torch, an open-source developer toolkit for machine learning tasks. Torch is used by numerous companies including Twitter, NVidia, AMD, and Intel. Torch has been best applied to deep learning and convolutional neural nets, which have been successful in understanding images and video. Earlier this year, Facebook made its optimised deep learning modules open-source. These modules are significantly faster than the default modules in Torch and allow developers to train larger neural nets in less time.

IBM — SystemML

In June, IBM — a company synonymous with AI with its Deep Blue and Watson systems — recently contributed SystemML, its machine learning platform to the fastest-growing open-source community, Apache Spark. IBM will offer Spark as part of its broader IBM Bluemix open cloud technology platform.

Google — TensorFlow

In November, Google released TensorFlow for free. TensorFlow is Google’s second-generation machine learning system, replacing DistBelief. The system represents computations as stateful dataflow graphs, making it easy to run networks across multiple machines with different hardware. Developed by the Google Brain team, including deep learning legend Geoffrey Hinton, it’s used in various Google products including Gmail and Photos. Its most high profile use is in the RankBrain system, Google’s AI engine that handles a substantial amount of Google’s search queries.

Microsoft — Distributed Machine Learning Toolkit (DMTK)

Finally, in November, just 3 days after Google, Microsoft open-sourced its framework and algorithms for distributed machine learning. The DMTK is designed to allow machine learning tasks to be easily scaled. The toolkit also includes LightLDA, an efficient algorithm for topic model training, and Distributed Word Embedding, a tool for natural language processing.

Software prices tend to zero as the value of data rises

Machine learning tools are making it easier to understand the abundance of data that is being collected. Deep learning techniques are enabling systems to learn from unstructured data. Much of the real world is messy, complex, and rarely fits nicely into the rows and columns that traditional approaches to intelligent machines, software, and databases require. Videos, unlabeled text, and voice are all being analysed by systems that can now infer context, making insights more accurate and valuable.

“While laggards in the industry debate the merits of on-premise servers versus cloud services and struggle to merge vast numbers of databases, technology leaders are pushing further ahead.”

Intellectual property is being handed over to the open-source community to use as they want. As most companies are just beginning to devise their Big Data strategies, Google, Facebook, Microsoft, and IBM have devised their strategies, built Big Data and machine learning tools, and are now giving them away for free.

Most companies consider their proprietary software to be a competitive advantage and how they provide value to customers. As traditional hardware companies are slowly trying to become software- and services-based companies, the ground beneath them has shifted.

Telcos are trying to adapt to a world of software-defined networking rather than routers and switches, and manufacturers are moving from providing tools and widgets to usage analytics and predictive maintenance. As they arrive in this new dawn of software and services with the promise of fat margins, they will find it was a mirage. Software on the Internet has almost zero marginal costs. Prices will trend to zero. The real value is data.

Using machine learning tools is hard

Google, Facebook, Microsoft, and IBM have not given away all of their software. Google, Microsoft, and IBM also have machine learning platforms through which they offer machine learning APIs to paying customers. These companies want to attract developers to build on their platforms to make it more valuable. They are open-sourcing their tools basically so developers can learn how to use them. This is great for future hiring and it fosters a thriving developer ecosystem.

Valuable platforms attract users and developers. Developers have limited resources and will only allocate resources to platforms which generate the greatest revenues. This is why small developers build iOS apps first, Android apps second, and Windows Mobile never. Platform dynamics are winner-takes-almost-all. Companies can court developers, pay them to build for the platform, and take a lower cut of sales; but if the platform doesn’t have users, it doesn’t matter. See Windows Mobile.

“The challenge for non-software companies trying to build platforms for their own customers is that open-source is not part of their culture.”

Customer value is created with machine learning applications from third-party developers providing new innovative services. To get developers on board, open-source will be the only way. Data will be the only sustainable competitive advantage.

Recent advice to the industry has been to move away from making physical things and to making digital things. However, charging for digital things on the Internet is harder than ever. With machine learning, making digital things is not even enough. Companies need to give away the digital things. This will be a bitter pill to swallow for the management and boards of many companies going through a digital transformation.

The only thing that matters now is data.

The Only Thing That Matters in Machine Learning is…

The Human Brain: The Final Commodity

“If, then, man’s principal asset and value is his brain and his ability to calculate, he will become an unsaleable commodity in an era when the mechanical operation of reasoning can be done more effectively by machines.”

Technology has always replaced humans

The philosopher, Alan Watts, wrote the above in 1951. The idea that machines will replace humans is not new. The job of sowing seed has become easier and more productive over time with the hoe, the plough and the tractor. The textile industry has seen the same productivity improvements. The spindle, the flying shuttle, the spinning mule and the spinning jenny. Machines continue to replace human manual labour. But technology and innovation has not led to sustained mass unemployment. More productive sectors of the economy create jobs as less productive areas lose jobs. The loss of human jobs to technology has been a feature of human progress since stone tools.

This time it is different

Artificial intelligence tools are arriving at a time of instant global communication and digital distribution. Facebook can make a breakthrough in New York and on the same day Shanghai and Santiago researchers can test the approach. Knowledge can be shared and reviewed with experts on Twitter. Amazon Web Services allows developers to build products faster than ever. Developers can add AI capabilities from IBM Watson with a simple piece of code. The iOS app store and Google Play Store allow apps to reach over 2.6 billion people today, growing to 6.1 billion people in 2020. The Industrial Revolution took around 100 years to transform the global economy. The artificial intelligence revolution will touch almost every person on earth in less than a few decades.

What happens when the jobs run out?

As machines replace the last humans on farms and in factories, the real revolution is coming to offices. The service sector provides jobs in which humans have maintained their competitive advantage: the brain. Digital Genius provides a human-like customer service tool that can replace customer service reps. Ellipse by Thoughtly is a research tool that analyses websites, journals and articles to discover insight. One single research manager with Ellipse can do the job of a team of ten researchers. Products like Guesswork can use AI to qualify, prioritise and route leads reducing the size of the sales team. A machine can complete most data analysis tasks better than a human. Millions of jobs are at threat in every part of the organisation. Data analysis is central to many jobs in marketing, sales, HR & recruitment, customer service, and finance.

What would you do if money were no object?

Over the next 20 years, there will be millions of people around the world that can no longer trade their labour for capital. Retraining programmes may work in some cases and more flexible labour laws may boost employment for a limited time. New technologies like drones, virtual reality and autonomous cars will generate new jobs. But these jobs will be highly-skilled and will need engineering ability. This will leave millions of under-skilled, un-skilled and wrongly skilled workers. Economic growth will no longer create jobs for humans. This is a fundamental break in the engine of capitalism.

A world of worklessness will force society to adapt. One transformative theory that has dismissed before is the Universal Basic Income (UBI). The UBI, first proposed by Thomas Paine in 1795, is not means-tested and every citizen is eligible. High costs and the disincentives to work resulted in the idea being dismissed. The age of worklessness will demand a review. AI applied across the economy will raise productivity levels to unprecedented levels. Healthcare, public transport, and energy will cost less and generate more. The challenge for governments is to ensure AI gains do not amass to a few trillion dollar companies, as millions of citizens cannot earn money. This is already becoming a concern for many governments. UBI will be affordable for most governments if redistribution becomes a policy goal.

If all humans have enough money to meet the majority of their needs, what will people do with their time? Scientists, artists, and designers will be free to pursue their passions without the need for money. Even Thomas Hobbes who described the world as “short, nasty and brutish”, believed leisure to be the mother of Philosophy. Oscar Wilde as always said it best; “cultivated leisure is the aim of man.” All humans may finally be free to pursue leisure and happiness.

What would you do if money was no object?

The Human Brain: The Final Commodity

Time to Start Taking Artificial Intelligence Seriously

Recent developments in artificial intelligence have been under-appreciated by industry due to a lack of clarity in definitions and a lack of understanding of machine learning. Machine learning, deep learning, neural networks, predictive analytics, big data analytics and artificial intelligence are used interchangeably leading to widespread confusion. In addition, the discussion around artificial intelligence has always been led astray by Hollywood and news media looking for exciting stories. There is nothing more exciting than machines vs humans story, and so important developments in artificial intelligence are lost in the Terminator fantasies. Developments in AI algorithms are often hard to explain, and because the word has been misused over the years, the public associate AI with an human-shaped robot in the future and dismiss stories of incremental progress in AI. This is a huge mistake.

Artificial intelligence has been improving software for a long time now, often going by the name of machine learning. Your Google search, Amazon recommendations and your SIRI requests. When an algorithm is successful, it is embedded in software and disappears making the software smarter over time. What then has changed to make AI so important today? The answer is threefold; big data, increased computation and parallel computing.

Vast amounts of data are being collected and stored at ever decreasing costs providing a wealth of training data for training for AI algorithms. With the rise of cloud computing and the continued progress of Moore’s Law, big data can be processed more cheaply and quickly than ever before. Finally, the use of graphical processing units (GPUs) and application specific integrated circuits (ASICs) has provided a more efficient way of running learning algorithms, which tended to be inefficient and ineffective on traditional central processing units (CPUs).

These three trends have provided a cheap innovation platform for developers who are now using artificial intelligence algorithms to make progress across of range of industries. The big internet companies such as Google, Facebook and Amazon have become multi-billion dollar businesses on the back of using AI as a competitive advantage in search, social and retail. These AI techniques are now cheap enough and pervasive enough that they can be applied in any industry. IBM, Google and Microsoft even offer these techniques to any company in a package called ML-as-a-service (MLaaS). The application of AI to meet specific market needs will form the basis on the next billion dollar companies. Already we can see, Uber in Logistics, Airbnb in hospitality and Palantir in data analysis. Human Longevity Inc, for example, are attempting to pull together all genomic, microbiome, metabolome, and physiological data from an individual and run a machine learning algorithm to better understand the human aging process.

The thing about artificial intelligence is that it is an exponential technology. The rate of improvement will continue to double every 18 months, costs of storage and computing will fall, and new neuromorphic chip designs will allow for more efficient machine learning computation. Extrapolating Moore’s Law even further, Ray Kurzweil, Director of Engineering at Google estimates that by 2023 it will cost $1000 for a computer with 20 petaFLOPS, roughly the same processing power as the human brain.

This exponential progress will creep up on businesses not paying attention. This will not be confined to the technology industry, AI will fundamentally reshape every industry. AI is the very definition of a disruptive technology.

Time to Start Taking Artificial Intelligence Seriously