The Future is Now

We are on the cusp of a new reality that will impact every aspect of our lives. Technological constraints are no longer an obstacle. In the world of computers and networks, almost anything is possible

Concepts from the world of technology such as social networks, mobile applications, business analytics and big data, cloud computing, artificial intelligence and machine learning, wearable computing, cognitive computing, advanced robotics, internet of things, virtual and augmented reality, 3D printing and more have revolutionized our way of life and the way that organizations do business. Managers of all organizations are now required to address new challenges arising from the advent of digital technology.

These technologies have led to the emergence of new business models such as sharing economy, crowdsourcing, ecommerce, data selling and others. At one of the recent Davos conferences, this bundle of changes was labelled the Fourth Industrial Revolution, a game changer that subverts commercial branches and blurs the boundaries between them – a phenomenon that can be dubbed the digital transformation.

The digital transformation poses an explicit threat to organizations, yet it also presents a remarkable opportunity. As such, addressing this transformation requires that organizations adopt a culture of business flexibility, continual awareness of changes in the business environment, an ability to make decisions based on an understanding of the inherent value of data, the promotion and encouragement of innovation, and a willingness to take risks. This is a profound business and organizational transformation, which consequently necessitates that the organization oversees it. These factors have all become a critical condition for the modern-day manager operating in a dynamic, global, digital environment.

The following review will cover a few of the new technologies that offer a dramatic potential for change – a change that has only just begun:

Artificial Intelligence – the transition to Broad AI

In recent years, humankind has been demonstrating an increasing interest in AI, and not for naught. The ability to leverage the data we possess, to extract new insight from this data in real time, and to communicate this through the use of human language is a game changer in practically every aspect of our lives.

The study of AI began in the mid-fifties, but has picked up steam in recent years. There are several reasons for this. First, the amount of data has grown with the introduction of social media and IoT (internet of things) to our lives. The ability to translate unstructured data (images or video) into insight unveils new, infinite opportunities. Secondly, significant progress has been demonstrated in the research of AI based on the amount of data for learning and developing technological tools such as machine learning and deep learning. Another reason that has propelled the technology forward is the increasing involvement of the business sector. Processes have become more efficient and automated, requiring less resources and leveraging organizations’ capabilities and the customer experience to new ground. It is therefore also easier to raise funds for R&D for additional groundbreaking technological tools.

Research indicates that we can now achieve better results in very specific tasks in the field of AI. Autonomous vehicles, face and emotion recognition, and even the personal adaptation of medication – these tasks are called Narrow AI. However, even the smartest autonomous vehicle cannot talk to us about the weather, unless its developers have programmed the vehicle with the capabilities required to undertake this specific task. The vehicle clearly cannot learn how to play a new card game after a brief explanation of the rules – something that any small child can do with ease.

Researchers’ long-term goal is to achieve General AI :a machine that ‘contains’ enough information, basic knowledge, and a very solid handle of language, allowing it to learn any new topic and to communicate in an entirely human manner in a minimal amount of time. It could be effortlessly taught to talk about the weather, to prepare for a major life event, or to play a new game. We are still far from reaching this goal, and it would necessitate significant advances combining an understanding of human language, modeling knowledge and information, and programming – integration that would help form logical deductions.

The next stage in technological advances is the field of Broad AI. Broad AI could use minimal effort to take models learned for one task and adapt them to be used in a new task. Conversely, it can begin a development process with very limited information, learn new information, and combine it with accumulated knowledge in order to succeed with more complex tasks. To this end, new technologies are being developed in labs. These technologies are called Active Learning (the ability of a computer to understand what it doesn’t know and to request additional examples), Transfer Learning (the ability to shift from learning in one field to learning in another), and Meta Learning (computers’ abilities to independently adapt the learning algorithm to existing data).

The blockchain revolution

The distributed ledger method at the core of blockchain technology amplifies the transparency and control of all involved parties, minimizes mediating factors, and cuts cost, risk and precious time – a huge asset for any organization. Moreover, this technology changes the way organizations communicate with customers, suppliers and competitors, creating new business procedures with significant potential for disruption. Accordingly, Gartner Consulting projects that by 2030, the added value of blockchain-based transactions will amount to no less than three trillion dollars.

Tech companies recognize the enormous potential of blockchain technology and work in collaboration with universities across the globe to develop applications in fields such as finance, education, information security, supply chain, and other fields where many of these technologies are accessible by the cloud and by open-source code.

Furthermore, not only does blockchain technology transform the way that organizations communicate with their ecosystem; it creates new models for business communications and helps extract new commercial value from organizational data. These are all core components of digital transformation. It is not a necessity to perform digital transformation in an organization in order to assimilate blockchain technology, but without a doubt, organizations that have already succeeded in beginning the transformation that is fundamental and required in our digital reality are slated to reap the benefits of what they have sowed in the preliminary feasibility stages.

The revolution of quantum computing

Quantum mechanics currently lies at the core of the upcoming revolution in the world of computing that is projected to have a significant impact on our lives – quantum computing. Quantum computers have incredible calculation capabilities that no other computers can contend with – not even supercomputers or the cloud, which is comprised of a large quantity of powerful computers. Technology giants are racing for quantum computing development that can solve a wide range of problems and that can be used extensively both by the scientific community and by the commercial scientific community.

Quantum computing will contribute to humankind in a variety of aspects. In the world of pharma, new medicine and materials will be discovered through a deeper understanding of molecular interactions and chemical processes. In the world of supply chain management and complex logistics tasks, quantum computing will offer the optimal path to actions that will ensure maximum efficiency and an improved customer experience. The industry of financial services could build new financial models through the precise identification of risk factors and through reaching better investment decisions. In the field of AI and in applications such as machine learning, quantum computers will be able to contend with particularly large collections of data and aid in instantly issuing vital insight. In the field of cloud security, the new technology will offer better privacy protection.

So why is quantum computing a game changer? While a regular computer bit can only display two potential values (0 or 1), a quantum bit (qubit) also displays a range of states that combine these two values. In effect, the number of potential states increases by magnitudes relative to events in the world of classical computing. This is an alternate mathematical infrastructure that uncovers infinite calculation possibilities and a number of simulations we have never seen before, generating possibilities for innovative solutions, as described above. Moreover, the time required for relatively complex calculations will drop dramatically, which will consequently save precious time and resources. In certain situations, in medicine, for example, time not only means money; it means life.

According to one assessment, the global quantum computing market will be valued at $10.7 billion by 2024, of which $8.45 billion will come from sales of products and services, and $2.25 billion from government funded R&D programs.

Looking to the future – Deep learning

Deep learning is a field of computational learning inspired by brain activity. Like the brain, deep learning utilizes multiple layers of neurons linked together within a ‘neural network.’ When data flows from one region of the network to another, they change structure and are integrated with other information along the way. By digesting and processing large quantities of complex data, the program learns to identify digital expression patterns of voices, sounds, images and text.

Today, deep learning is a remarkably popular concept in the world of computing, and as such, it is important to remember that its core principles have been around for decades. With the promotion of the concept of quantum computing, deep learning has been playing a key role in the next generation of AI applications rapidly emerging around us.

The most fascinating aspect of deep learning is the fact that it has no need for human intervention in order to display information through a collection of characteristics that serve as the basis for computer learning. In deep learning, computers are exposed to large quantities of data that allow it to independently learn the expression it will use to learn how to perform the task in the best way possible. The process is similar to the way that a parent can teach an infant how to distinguish a dog from a cat: the knowledge comes after the infant has observed a large number of examples on their daily ride in the stroller when they hear their mother or father call the animal they see a cat. The parent does not need to introduce a defined rule to the infant by which “if it is furry, it meows, and it rummages through garbage; it is a cat.”

Other than the availability of an unprecedented scale of data, another notable development that facilitates deep learning is powerful, dedicated computing that allows computers to run complex learning algorithms, which can be used to effectively learn from large quantities of information. The computer learns what to search from the information presented to it; it then builds a model that it can use to solve an identical problem in a new situation.

As consumers, we are already reaping the benefits of deep learning on our smartphones. For example, deep learning is used to improve voice search capabilities even when users are located in noisy environments. Virtual agents, also known as chatbots, are already well known, and we haven’t even discussed our growing expectations of autonomous vehicles and the revolution they will spark.

Israel’s hi-tech industry recognized the inherent potential of deep learning. For example, in the field of computer vision, deep learning allows cars to monitor the road in front of them and to detect close vehicles, to alert drivers about pedestrians that might step off the curb at any moment, or to understand natural language commands by drivers. Other Israeli companies have created advanced deep learning algorithms that improve face detection capabilities, malware file detection through cybersecurity systems, or the supply of financial consulting that leans on the infinite wealth of information and data accessible outside the organization across the web.

The financial and commercial potential offered by the world of deep learning is particularly extensive. In order to facilitate natural communication with computers, we must study these computers in order to understand human speech in a variety of different accents. Computers must learn how to build sentences from scratch, to identify who is entering a particular room, and when someone expresses themselves cynically or sarcastically, to know how to discern between this type of expression and identical text uttered without a trace of cynicism. IBM’s research lab in Haifa is currently working on a series of projects in this field.

The revolution of autonomous vehicles

Our interest in the image of the smart city, in all its facets, especially in terms of smart transportation, makes us feel like we are reading a riveting sci-fi novel that we are a part of. The thought that we are on the cusp of a new reality that will impact every aspect of our lives is exhilarating. Technological constraints are no longer an obstacle. In the world of computers and networks, almost anything is possible, unlike the old world where we still needed products based on metal, plastic and leather for taxing manufacturing processes abundant with engineering, planning and stabilization. Is this possible?

It is no wonder that of all the components in the fabric of our lives, the smart vehicle comprises such a significant share of our deliberation of future virtualization. The reason for this can be found in several aspects: first, the position of the vehicle in our lives. While for some, the vehicle serves for the convenience of independent mobility alone with no significance beyond this purpose, for others, a vehicle serves in their direct life environment as an almost living entity. Something used to reflect social status, a topic of conversation between friends, not to mention an object to take care of and treasure. The position of the vehicle in our new reality is derived from precisely these two perspectives. For those seeking convenient mobilization solutions, the smart vehicle will be no more than an ‘elevator’ in the three-dimensional sphere. Reading will be performed with the use of a device similar to a cellphone, and the mobilization service will be completed (and paid for) towards the required destination with the utmost convenience and efficiency. For those who ‘raise’ their vehicle like a pet, the vehicle will be upgraded to the position of ‘personal assistant.’ Knowing all aspects of its master’s daily routine, personal preferences (both overt and covert), and the map of present and projected constraints on the road, the vehicle would be pleased to advise, remind, coordinate and update in a continuous, pleasant manner, all the while autonomously, smoothly moving from destination to destination.

Secondly, the position of the vehicle as a bridge between past and future technologies, in that despite all the innovations, we will not forget our most basic demands for safety, reliability, comfortable travel conditions, and reasonable travel costs. All of these are deeply ingrained in traditional vehicle technology that has developed over the past 120 years, and is still led by the large, well-known vehicle industries in Europe, the US, and the Far East. Another question briefly raised on international debate forums: who will be the key players in the manufacturing of future vehicles? Vehicle manufacturers that will integrate new IT technologies in their flagship products, or software companies such as Google and Apple who will integrate engines, tires and gearboxes in communications network based cyber solutions? As of today, it appears that the answer to the question is connected to the debate on the first aspect. The management of autonomous public transportation routes operating as ‘elevators’ will be conducted by social media and software companies through mechanical motion products to be constructed from available modular components, while personal vehicles operating as ‘personal assistants’ will continue to be built by industries familiar to us, with software and network applications integrated based on adjustable individual planning and design.

The third aspect relates to the degree of autonomy of vehicles in the public sphere. While public opinion dictates full autonomy of all vehicles in this sphere looking no further than 2-3 decades to the future, the consensus in the industry is that the difficult challenge lies in the interim period, which will be characterized by a combination of autonomous and manual vehicles on the road at the same time. On the one side, there is a robotic, autonomic, predictable, calculated, ‘level-headed’ system; and on the other, there is a human driver – an unpredictable, improvising player who at times reacts emotionally and illogically (in warm, humid climates more than in northern, colder regions). Furthermore, the foundation of the cyber technology of autonomous vehicles is connectivity; the continuous flow of information at remarkable rates between all players sharing the network. In a reality where every vehicle ‘shares’ all the details in its environment that provide relevant information as a condition for reaching collaborative decisions both as individuals and as a group, human drivers will be ‘autistic’ beings that are disconnected at both ends: in updating members of the network, and in reaching decisions or guidelines for future implementation. We see the implications through the immense investments in multidimensional sensor systems that are wholly dedicated to providing early alerts about unpredictable behavior by a manually driven vehicle in the area. At the time of full autonomous driving, these systems will of course be obsolete, as will the entire principle of sensors, because every individual will be aware of the intentions of the individuals in their environment before any event occurs, due to the connection through a holistic system. This is analogous to the way our brain is aware of our hand movement before it moves, and it does not learn about this movement from sight.

The fourth aspect relates to cyber defense. Like other fields in our emerging autonomous, connected environment, in terms of smart transportation, cyber security considerations carry critical weight in two dimensions: safety/security and trust. Transportation systems are characterized by potentially high risk to life by definition, whether due to crowdedness in the two-dimensional urban sphere, or due to falling and crashing in the vertical dimension. Many years of investment in technological reliability on the one hand, and human training on the other, have brought the percentage of accidents to a very low number and a socially sustainable level. Behavioral deviation of individuals, even if extreme, is limited to isolated events that do not trickle down to other events, as severe as they may be. This characteristic of reality can change dramatically during a cyberattack on the extensive network system. For example, offensive cyberattacks that cause the ‘irrational’ behavior of hundreds of autonomous vehicles can bring about a disaster on the scale of a regional natural disaster. However, beyond the fatalities, it can be assumed that in contrast with natural disasters, such an event would lead to society losing faith in smart solutions as a whole, inevitably leading to a human-machine crisis that is difficult to foresee.

The way to deal with this risk is to assume feasibility in early planning stages, to integrate solutions with independent cyclic redundancy at the productization stages, while at the same time, establishing examination areas with authentic characteristics of holistic reality, in which the immunity of the full systems can be examined for a range of attacks in a perpetual, continuous manner.