7 Innovative Digital Transformation Trends for 2021

Change is one constant in life. For the world of technology, change only means one thing—pushing the boundaries in search of more innovative developments.  Digital transformation (DX) is the unification of digital technology and a business, company, or organization. Digitizing various processes can boost efficiency and allow services and products to improve by leaps and bounds.  In 2021, there’s no way for any organization to stay relevant without keeping up with these new DX trends:
  • Cybersecurity

Whatever the industry, security is one of the most important considerations, especially since so many people are now working remotely. Consequently, so many processes have migrated online. These days, financial fraud or theft happens online more often than not, and data breaches cost companies an average of $3.86 million in 2020. Thus, implementing new and improved cybersecurity measures is crucial in fighting back.  The 2021 cybersecurity trend is a movement from reaction to proactive prevention. The focus now is on thwarting hackers’ attacks, rather than trying to mop up the mess. One approach is the zero-trust security model (ZTA). This is a system that works off the assumption that all network devices are untrustworthy and rely on authorization policies, authentication steps, and strict access control to mitigate risks. An example of this is Microsoft’s implementation of a ZTA security model a few years ago. The increasing use of mobile computing, Bring Your Own Device (BOYD) policies, cloud-based services, and the internet of things (IoT) prompted the move. Initially, the scope included common corporate services on Windows, MacOS, Android, and iOS, which are then used by the corporation’s employees, partners, and vendors. The corporation then expanded the focus to include all applications used on Microsoft. The corporation introduced the use of smart-card multi-factor authorization (MFA) to control administrative server access. It later extended this to include users who access resources beyond the corporate network. Eventually, the smart cards were exchanged for a phone-based challenge and the Azure Authenticator application. Microsoft also implemented device verification, access verification, and service verification. 

  • Data Analytics

One universal concern for businesses is the generation of revenue, and to grow that, they need to know what works and what doesn’t. The world is moving towards automation of processes and an increased use of AI to perform tasks that were previously manually performed.  Since the COVID-19 pandemic, many businesses have by necessity moved online; apart from this, the traditional AI techniques that relied on previous statistics and information are potentially becoming ineffective because of the vast changes of the past year. AI systems are heading towards working with “small data” instead of historical data. Data science developments and the use of cloud technology are closely linked, especially when it comes to using data as a service (DaaS), granting on-demand access to information without relying on proximity. Phoenix-based mining company Freeport-McMoRan is a fine example of how data-driven decisions can transform a business. Chief operating officer Harry ‘Red’ Conger told McKinsey Digital that real-time data, AI, and the company’s veteran metallurgists’ and engineers’ institutional knowledge combined to lower operating costs, bolster economic resilience, and speed up decision-making. In 2018, the company unveiled a $200 million plan to expand the capacity of its Bagdad, Arizona, copper mine. When copper prices plunged a few months later, however, they scrapped the plan. Not long after, the company started building an AI model that could boost productivity. Data scientists analyzed and challenged existing operations, and AI showed how they could better use equipment. Working together, the data scientists, engineers, and metallurgists made changes that led to the mine’s processing rate to increase by 10%. The company has since implemented the AI model in eight of its other mines. 

  • Democratization of Innovation

Democratization in the digital transformation field refers to the shift away from a provider-focused model towards a user-focused one.  Companies have a tendency to create fairly uniform products that are quickly being pushed aside by individuals’ and small businesses’ desires for innovations that are specific to their field. Developments are no longer going to be withheld by a smaller clique of establishments but made available to a much wider group of users.  According to the WorldBlu list of freedom-centered organizations, New Belgium Brewing in Fort Collins, Colorado, is a business that has democratized innovation. Founder Kim Jordan said the brewery involves all its workers in the business’ strategic planning every year. Jordan also encourages open communication, trust, engagement, and inclusivity, and has an open-book approach to management.

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  • The Cloud

Many people use the cloud for personal activities, and businesses already know the importance of using its capabilities to their full potential. In the world of COVID, where so many employees have transitioned to remote work, the worldwide ease of access to the cloud is imperative. In 2021, technology is moving towards the use of multi-tenant clouds and hybrid cloud models.  Organizations are letting go of the belief that it’s best to use a single cloud vendor and are venturing into the area of hybrid clouds. It’s predicted that by 2022, over 90% of companies will have transitioned to hybrid cloud technology consisting of private clouds, various public clouds, and legacy platforms. Toyota offers an example of how cloud technology can enhance its offering. The vehicle manufacturer used the cloud to transform its cars from regular vehicles into connected platforms. Apps connect the cars to Microsoft Azure-hosted social media sites to track electric vehicle reward points and to offer other elements that improve the customer experience.  

  • Real-Time Data Processing

Financial establishments that required immediate data processing and updates to provide a service primarily made use of this technology. There’s an ever-increasing need for the instant delivery of answers—within a second or two.  Change in the business and technology sectors is constant and accelerating, and that requires real-time processing; processing that is not only fast but automated and reliable too. Development in this field gears itself towards flexibility, cost-effectiveness, and scalability.  Nissan uses real-time data analytics to make decisions that are appropriate for local markets. The automobile manufacturer uses Google analytics e-commerce tracking to obtain product preference data, such as the colors, models, and categories of the vehicles that are in demand in each market. The company uses Hortonworks Data Platform for its data lake, which includes driving, quality, and other data from across the company’s operations.

  • Contactless Solutions

Another effect of COVID has been the drastic shift away from face-to-face interactions towards virtual connections—from family get-togethers to business meetings. Virtual meeting platforms such as Zoom have taken off because of social distancing.  Contactless solutions also include contactless payments, which are becoming more and more popular for businesses providing a public service. Tap to pay debit or credit cards has almost become the norm, and they do away with the need for multiple people to handle the same payment terminal.  Apart from contactless payments, there’s also been a surge in contactless fulfillment. This digital development has seen the rise of online shopping. This isn’t only about purchasing items on sites like Amazon, but also ordering groceries and other smaller products from local businesses and receiving them by door-to-door delivery or arranging curbside pickup. Many small, medium and micro enterprises (SMMEs) are among the South African businesses that have seen how contactless options can transform companies’ operations. Nedbank launched tap-on-phone functionality for SMMEs in October 2020, and customers have responded well to the option of tapping their cards rather than swiping them and entering their PIN number. A MasterCard study indicated that 75% of South Africans used contactless payment methods when given the opportunity. 

  • Development and Wider Launch of 5G

Contrary to what conspiracy theorists believe, 5G is a massively positive development that’s set to revolutionize internet use by upgrading response time, improving speed drastically, and granting greater ease of access for multiple connected devices.  5G promises to deliver to both telecom operators and users. For individuals, the step up from 4G will bring vast improvements in internet access through a low latency rate and larger access areas. No more endlessly searching for Wi-Fi or having to move your laptop around in the hopes of a stronger signal. For operators, 5G offers the ability to shift their value proposition, allowing them to change from network capacity providers to full-scale, innovative digital partners. The spectrum for growth is huge, as are the opportunities for increasing revenue.  Samsung is one of the biggest brands globally already capitalizing on 5G. The brand’s range of Galaxy 5G devices takes connectivity to new heights and has set the benchmark for other mobile manufacturers to follow. 

Looking to the Future

The past 18 months have brought about a multitude of changes, challenges, and crises for individuals and businesses alike. Some industries have suffered more than others, but it seems unlikely that things will go back to “normal,” at least in the next few years.  There are speculations that the world is about to enter an age of pandemics, which will cement the current trend of remote working, online shopping, and reduction in face-to-face interactions across all industries. These changes have forced industries to innovate at a rate that has previously been difficult to sustain. Change has become the order of the day, and to survive, organizations have had to dedicate every effort to stay ahead of the curve.

Evolve to Thrive

Every species undergoes evolution over centuries and millennia, and technology is no different. The speed of change may differ, but the purpose is the same: to adapt to an unstable environment and prepare for the future.  These digital transformation trends will positively drive change and pave the way for new developments that will build on existing structures.

Digital Transformation Roadmap for Businesses

The following map is one approach to digital transformation for an organization. By following these steps, your organization can update practices and gain a competitive edge over those who’ve yet to embark on the transformation process. Step 1 – Develop innovative business models and experiences. Step 2 – Encourage a digital DNA culture within the organization. Step 3 – Update existing infrastructure with new technologies. Step 4 – Use data, not gut reactions, to drive the decision-making process. Step 5 – Find and collaborate with innovative and creative partners.

Ready to start your digital transformation journey?  

Digital transformation is at the core of pushing the business forward. When done the right way, it can help businesses achieve sustainable growth and stay ahead of their competition. Building a successful digital transformation strategy doesn’t have to be a challenge. With the right support and expertise, you can adapt to change and outpace evolving demands. Xaltius is a trusted partner for these projects.  Our experience has proven that a successful digital transformation strategy needs to focus on two things. First, your strategy must include ways to manage evolving business goals. These strategies also need to account for the cultural change that comes with those advancements. Our Data Scientists provide the kind of external perspective, agility, and understanding required for real innovation.   When you partner with Xaltius, you’ll have access to highly skilled professionals, Business Analysts, Data Engineers, Corporate Trainers, and all the ancillary roles for the delivery of your strategy. Each of us at Xaltius is directly accessible to your project managers. We are a Singapore-based IT consultancy providing customized, cost-effective IT solutions to enterprises.  Let us know if we can do anything for you.  To learn more about what we can do for your organization, talk to an industry expert – Book a consultation.

About the Author: This article is written by Kristie Wright. Kristie Wright is an experienced freelance writer who covers topics on logistics, finance, and management, mostly catering to small businesses and sole proprietors. When she’s not typing away at her keyboard, Kristie enjoys roasting her own coffee and is an avid tabletop gamer.

Renowned Data Science Personalities

With the advancement of big data and artificial intelligence, the need for its efficient and ethical usage also grew. Prior to the AI boom, the main focus of companies was to find solutions for data storage and management. With the advancement of various frameworks, the focus has shifted to data processing and analytics which require knowledge of programming, mathematics, and statistics. In more popular terms, this process today is known as Data Science. Few names stand out and have a separate base of importance when the name data science comes into the picture, largely due to their contributions to this field and who have devoted their life and study to reinvent the wheel. Let’s talk about some of the best data scientists in the world.


Andrew Ng

Andrew Ng is one of the most prominent names among leaders in the fields of AI and Data Science. He is counted among the best machine learning and artificial intelligence experts in the world.  He is an adjunct professor at Stanford University and also the co-founder of Coursera. Formerly, he was the head of the AI unit in Baidu. He is also an enthusiast researcher, having authored and co-authored around 100 research papers on machine learning, AI, deep learning, robotics, and many more relevant fields. He is highly appreciated in the group of new practitioners and researchers in the field of data science. He has also worked in close collaboration with Google on their Google Brain project. He is the most popular data scientist with a vast number of followers on social media and other channels.

DJ Patil

The Data Science Man, DJ Patil, needs no introduction. He is one of the most famous data scientists in the world. He is one of the influencing personalities, not just in Data Science but around the world in general. He was the co-coiner of the term Data Science. He was the former Chief Data Scientist at the White House. He was also honored by being the former Head of Data Products, Chief Scientist, and Chief Security Officer at LinkedIn. He was the former Director of Strategy, Analytics, and Product / Distinguished Research Scientist at eBay Inc. The list just goes on.

DJ Patil is inarguably one of the top data scientists around the world. He received his PhD in Applied Mathematics from the ‘University of Maryland College Park’.

Kirk Borne

Kirk Borne has been the chief data scientist and the leading executive advisor at Booz Allen Hamilton since 2015. Working as a former NASA astrophysicist, he was part of many major projects. At the time of crisis, he was also called upon by the former President of the US to analyze data post the 9/11 attack on the WTC in an attempt to prevent further attacks. He is one of the top data scientists to follow with over 250K followers on Twitter.

Geoffrey Hinton

He is known for his astonishing work on Artificial Neural Networks. Geoffrey was the brain behind the ‘Backpropagation’ algorithm which is used to train deep neural networks. Currently, he leads the AI team at Google and simultaneously finds time for the ‘Computer Science’ department at the ‘University of Toronto’. His research group has done some overwhelming work for the resurgence of neural networks and deep learning.

Geoff coined the term ‘Dark Knowledge’.

Yoshua Bengio

Having worked with AT&T & MIT as a machine learning expert, Yoshua holds a Ph.D. in Computer Science from McGill University, Montreal. He is currently the Head of the Montreal Institute for Learning Algorithms (MILA) and also has been a professor at Université de Montréal for the past 24yrs.

Yann LeCun

Director of AI Research at Facebook, Yann has 14 registered US patents. He is also the founding director of NYU Center for Data Science. Yann has a PhD in Computer Science from Pierre and Marie Curie University. He’s also a professor of Computer Science, Neural Science and the Founding Director of the Data Science Center at New York University.

Peter Norvig

Peter Norvig is a co-Author of ‘Artificial Intelligence: A Modern Approach’ and ‘Paradigms of AI Programming: Case Studies in Common Lisp’, some insightful books for programming and artificial intelligence. Peter has close to 45 publications under his name. Currently the ‘Engineering Director’ at ‘Google’, he has worked on various roles in Computational Sciences at NASA for three years. Peter received his Ph.D. from the ‘University of California’ in ‘Computer Science.’

Alex “Sandy” Pentland

Named the ‘World’s Most Powerful Data Scientist’ by Forbes, Alex has been a professor at MIT for the past 31 years. He has also been a chief advisor at Nissan and Telefonica. Alex has co-founded many companies over the years some of which include Home, Sense Networks, Cogito Corp, and many more. Currently, he is on the board of Directors of the UN Global Partnership for Sustainable Data Development.

These are some of the few leaders from a vast community of leaders. There are many unnamed leaders whose work is the reason why you have recommender systems, advanced neural networks, fraud detection algorithms, and many other intelligent systems that we seek help to fulfill our daily needs.

Ethical issues in Artificial Intelligence – Problems and Promises

With the growth of Artificial Intelligence (AI) in the 21st century, the ethical issues with AI grow in importance along with the growth in the technology. Typically, ethics in AI is divided into Robo-ethics and Machine-ethics. Robo-ethics is a concern with the moral behaviour of humans as they design and construct artificially intelligent beings, while Machine-ethics relates to the ethical conduct of artificial moral agents (AMAs). In the modern world today, the countries are stockpiling weapons, artificially intelligent robots and other AI driven machines. So, analysing risks of artificial intelligence like whether it will overtake the major jobs and how can its uncontrolled and unethical usage can affect the humanity also becomes important. And to prevent humanity from the ill-effects and risks of artificial intelligence, these ethics were coined.

AI and robotics are unarguably one of the major topics in the field of artificial intelligence technology. Robot Ethics or more popularly known as roboethics is the morality of how humans interact, design, construct, use, and treat robots. It considers how artificially intelligent beings (AIs) may be used to harm humans and how they may be used to benefit humans. It emphasizes the fact that machines with artificial intelligence should prioritize human safety above everything else and keeping human morality in perspective.

Can AI be a threat to human dignity?

It was the first time in 1976 when a voice was raised against the potential ill-effects of an artificially developed being. Joseph Weizenbaum argued that AI should not be used to replace people in position that require respect and care, such as:

  • A customer service representative
  • A therapist
  • A soldier
  • A Police Officer
  • A Judge

Weizenbaum explains that we require authentic feelings of empathy from people in these positions. If machines replace them, they will feel alienated, devalued, and frustrated. However, there are voices in support of AI when it comes to the matter of partiality, as a machine would be impartial and fair.

Biases in AI System

The most widespread use of AI in today’s world is in the field of voice and facial recognition and thus AI bias cases are also increasing.  Among many systems, some of them have real business implications and directly impact other people. A biased training set will result in a biased predictor. Bias can always creep into algorithms in many ways and it poses one of the biggest threats in AI. As a result, large companies such as IBM, Google, etc. have started researching and addressing bias.

Weaponization of Artificial Intelligence

As questioned in 1976 by Weizenbaum for not providing arms to robots, there stemmed disputes regarding the fact whether robots should be given some degree of autonomous functions.

There has been a recent outcry about the engineering of artificial intelligence weapons that have included ideas of a robot takeover of humanity. In the near future of AI, these AI weapons present a type of danger far different from that of human-controlled weapons. Powerful nations have begun to fund programs to develop AI weapons.

“If any major military power pushes ahead with the AI weapon development, a global arms race is virtually inevitable, and the endpoint of this technological trajectory is obvious: autonomous weapons will become the Kalashnikovs of tomorrow“, are the words of a petition signed by Skype co-founder Jaan Tallinn, and many MIT professors as additional supporters against AI Weaponry.

Machine Ethics or Machine Morality is the field of research concerned with designing of Artificial Moral Agents (AMAs), robots and artificially intelligent beings that are made to behave morally or as though moral. The sci-fi director Isaac Asimov considered the issue in the 1950s in his famous movie – I-Robot. It was here that he proposed his three fundamental laws of machine ethics. His work also suggests that no set of fixed laws can sufficiently anticipate all possible circumstances. In 2009, during an experiment at the Laboratory of Intelligent Systems in the Polytechnique Fédérale of Lausanne, Switzerland, robots that were programmed to cooperate eventually learned to lie to each other in an attempt to hoard the beneficial resource.

Concluding, Artificial Intelligence is a necessary evil. Artificial Intelligence-based beings (friendly AIs) can be a gigantic leap for humans in technological development. It comes with a set of miraculous advantages. However, if fallen into the wrong hands, the destruction can be unimaginable and unstoppable.  As quoted by Claude Shannon, “I visualize a time when we will be to robots what dogs are to humans, and I’m rooting for the machines.”Thus ethics in the age of artificial intelligence is supremely important.

Quantum Computing – The Unexplored Miracle

What is Quantum Computing?
Quantum computing is the use of quantum-mechanical phenomena such as superposition and entanglement to perform computation. A quantum computer is specifically used to perform such calculation, which can be implemented theoretically or physically. The field of quantum computing is a sub-field of quantum information science, which includes quantum cryptography and quantum communication. The idea of Quantum Computing took shape in the early 1980s when Richard Feynman and Yuri Manin expressed the idea that a quantum computer had the potential to simulate things that a classical computer could not.

The year 1994 saw further development of Quantum Computing when Peter Shor published an algorithm that was able to efficiently solve problems that were being used in asymmetric cryptography that were considered very hard for a classical computer. There are currently two main approaches to physically implementing a quantum computer: analog and digital. Analogue methods are further divided into the quantum simulation, quantum annealing, and adiabatic quantum-computation.

Basic Fundamentals of Quantum Computing
Digital quantum computers use quantum logic gates to do computation. Both approaches use quantum bits or qubits. These qubits are fundamental to Quantum Computing and are somewhat analogous to bits in a classical computer. Like a regular bit, Qubit resides in either 0 or 1 state. The specialty is that they can also be in the superposition of 1 and 0 states. However, when qubits are measured, the result is always either a 0 or a 1; the probabilities of the two outcomes depends on the quantum state they were in.

Principle of Operation of Quantum Computing
A quantum computer with a given number of quantum bits is fundamentally very different from a classical computer composed of the same number of bits. For example, representing the state of an n-qubit system on a traditional computer requires the storage of 2n complex coefficients, while to characterize the state of a classical n-bit system it is sufficient to provide the values of the n bits, that is, only n numbers.

A classical computer has a memory made up of bits, where each bit is represented by either a one or a zero. A quantum computer, on the other hand, maintains a sequence of qubits, which can represent a one, a zero, or any quantum superposition of those two qubit states; a pair of qubits can be in any quantum superposition of 4 states, and three qubits in any superposition of 8 states. In general, a quantum computer with n qubits can be in any superposition of up to different states. Quantum algorithms are often probabilistic, as they provide the correct solution only with a certain known probability.

What is the Potential that Quantum Computing offers?
Quantum Computing is such a unique field that very few people show their interest in it. There is a lot of room for development. It has a lot of scope. Some of the areas in which this is penetrating today are:

  • Cryptography – A quantum computer could efficiently solve this problem using multiple algorithms. This ability would allow a quantum computer to break many of the cryptographic systems in use today
  • Quantum Search – Quantum computers offer polynomial speedup for some problems. The most well-known example of this is quantum database search, which can be solved by Grover’s algorithm using quadratically fewer queries to the database than that is required by classical algorithms.
  • Quantum Simulation – Since chemistry and nanotechnology rely on understanding quantum systems, and such systems are impossible to simulate efficiently classically, many believe quantum simulation will be one of the most important applications of quantum computing.
  • Quantum Annealing and Adiabatic Optimization
  • Solving Linear Equations – The Quantum algorithm for linear systems of equations or “HHL Algorithm,” named after its discoverers Harrow, Hassidim, and Lloyd, is expected to provide speedup over classical counterparts.
  • Quantum Supremacy

In conclusion, Quantum computers could spur the development of breakthroughs in science, medication to save lives, machine learning methods to diagnose illnesses sooner, materials to make more efficient devices and structures, financial strategies to live well in retirement, and algorithms to direct resources such as ambulances quickly.  The scope of Quantum Computing is beyond imagination. Further developments in this field will have a significant impact on the world.

AR in the Education Industry

What is Augmented Reality?
Augmented reality abbreviated as AR is an interactive experience of a real-world environment where the objects that reside in the real-world are enhanced by computer-generated perceptual information, sometimes across multiple sensory modalities, including visual, auditory, haptic, somatosensory and olfactory. The information can be additive (adding more feel to the environment) or destructive (masking off the unnecessary natural environment).

Some common examples of the vast use of AR are mobile games like Pokemon Go and the popular social media photo app, Snapchat. These apps use AR for analyzing real-time user surroundings and further enhance user experience.

AR exhibits certain similarities with VR but has quite a few differences as well. Virtual Reality (VR) is entirely based on the virtual reception of the information, while in Augmented Reality (AR), the user is provided with more computer-generated information that enhances the perception of reality.

Taking a real-life example, VR can be used to create a walk-through simulation of a building under construction while AR can be used to show the building’s structures on a live view.

Uses of AR in Education
The field of AR recently showed some massive development after the immense popularity of apps like Pokemon Go. Recent upgrades have started to find its uses in the vast education industry. The traditional method of education is slowly becoming obsolete, and with the increasing technological growth, the education system is being digitized. The education technology industry giant, namely EdTech, is slowly adopting the use of AR and is predicted to reach around $252 billion by 2020, growing at a 17% annual rate.

Augmented Reality serves several purposes. It helps the students acquire, remember, and process the information. Additionally, AR makes learning easy and fun. Its use is not limited to the pre-school level but can be used equally till college and even at work.

Benefits of Augmented Reality
Due to a large number of benefits of Augmented Reality, its usage has become very frequent in learning. Its main advantages are –

  • Accessibility of Material – Augmented reality has the unique potential to replace the traditional paper textbooks, physical models, or printed manuals. It offers portable and less expensive learning materials. As a result of this, education becomes more accessible and mobile.
  • No special equipment requirements – Apart from a typical smartphone, Augmented Reality doesn’t need any more sophisticated equipment as in the case of VR.
  • Higher Engagement and Interest – Interactive AR based learning has a significant impact on students helping them in understanding and remembering the concepts for a more extended period.
  • Faster and effective Learning Process – Through visualization and immersion in the subject, AR ensures that the concept is deeply instilled in mind. A picture is worth a thousand words, Isn’t it? So instead of thousands of words of theory, the user can visualize the matter with their own eyes.
  • Practical Learning – The use of AR in professional learning gives an accurate reproduction of in-field conditions that can help in mastering the practical skills required for a specific job.
  • Improved Collaboration Capabilities – AR offers vast opportunities to diversify and shake up boing classes. Interactive lessons involving the whole level at the same time help in building qualities of team-work.
  • Safe and Efficient Workplace – Consider the field of heart surgery or a Space Shuttle. Without the introduction of actual dangerous equipment, the students can be taught in real life, how to solve problems.
  • Universality – Augmented Reality applies to any form of education and can significantly enhance the learning experience.

Challenges faced by Augmented Reality

There are certain challenges that you should take into account while using Augmented Reality:

  • Necessary Training Required – Conventional Teachers can find it difficult using new technologies into practice. Only the innovative and open-minded teachers will be ready to apply Augmented Reality in education.
  • Hardware Dependency – Requirement of AR equipment is necessary to make full use of this technology. All the students might not have a smartphone capable of supporting AR applications.
  • Platform-Based Issues – The AR app built must run equally well on the various available platforms.

 

Examples and Use Cases

The most popular application of Augmented Reality is unarguably in the field of education.

  1. It can help a teacher explain using a visual representation of the subject, which would help the students understand a subject better.
  2. Another case of Augmented Reality is distance learning. Students can learn even outside the classroom anytime, anywhere.

 

On a final note, Augmented Reality is a blessing in the education industry. It is not only beneficial to the students but also makes the work of teachers more comfortable and convenient.

Artificial Intelligence & Autonomous Vehicles – The future of transport

Almost everybody has experienced artificial intelligence of one level or the other by using everyday things around them. The next big thing everybody is looking forward to is revolution in the automated mobility industry. In 2016, Apple Chief Executive Tim Cook described the challenge of building autonomous vehicles as “the mother of all” AI projects.

While big players like Google, Uber, and Tesla are competing with other each and other prominent companies, investing billions to come up with a commercially successful fleet of driverless cars, AI experts believe that it may take many a year before self-driven vehicles can successfully conquer the unpredictability of traffic.

AI plays the main role, as always

An autonomous car can be defined as a vehicle capable of navigating itself without human help, using various sensors to perceive the surrounding environment accurately.  They can make use of a variety of techniques including radar, laser light, GPS, odometry, and computer vision.

Complex algorithms, cameras and LIDAR sensors are made use of to create a digital world that orients the self-driven car on the road and helps identify fellow cyclists, vehicles and pedestrians. It is extremely difficult to design and produce such systems (Find out how Xaltius’s Computer Vision Team is building new innovative solutions). They must be programmed to cope with an almost limitless number of variables found on roads. The autonomous vehicle industry therefore looks to machine-learning as the basis for autonomous systems. That is because huge amounts of computing power are required to interpret all of the data harvested from a range of sensors and then enact the correct procedures for constantly changing road conditions and traffic situations.

Deep learning and computer vision systems can be ‘trained’ to drive and develop decision-making processes like a human. Humans naturally learn by example and this is exactly what computers are taught to do as well, ‘think like humans’.

What is deep learning? – Deep learning is a method that uses layered machine-learning algorithms to extract structured information from massive data sets (Read our blog on AI vs ML vs DL). It is a key technology behind driver-less cars, enabling them to recognize a stop sign, or to distinguish a pedestrian from a lamppost. Each self-driven car is programmed to capture data for map generation, deep learning and driving tasks while they move along the traffic.

Autonomous vehicle industry developments

Google launched its self-driving car project in 2009, being one of the first to invest in this stream. Sensing that autonomous vehicle technology can open up a huge market and disrupting the current one, other tech-giants like Intel, IBM and Apple as well as cab hailing companies- Uber and Lyft and car makers have joined the race.

Alphabet’s Waymo, the self-driving technology development company was launched in December 2016. Waymo has been testing its vehicles Arizona for a little more than a year now. Places like California, Michigan, Paris, London, Singapore, Beijing among others regularly witness test-drives by self-driven cars.

The ground reality

While test-drives have become common in these places, the people have not yet adjusted to it. Research conducted by British luxury car maker Land Rover shows that 63% of people mistrust the concept of driverless cars. They are programmed to drive conservatively. While under the right conditions, it can eliminate aspects of human error and unpredictability like speeding, texting, drunken driving, when they move along with human drivers, the same unpredictability can confuse the autonomous cars. This could lead to accidents as well as a general mistrust over the technology. In March 2018, a self-driving Uber Volvo XC90 operating in autonomous mode struck and killed a woman named Elaine Herzberg in Tempe, Arizona. It is clear from regular reporting of accidents that happen during test-drives that autonomous car technology has a long way to go. Even after succeeding to avoid accidents, self-driven cars will have to face more than a decade long transition period, where humans have to accept this technology as well as give up driving.

This blog was written by our Content Writing Intern – Rona Sara George. Click on the name to view her LinkedIn profile.

Author: Xaltius (Rona Sara George)

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