Article | March 15, 2021
Stephen Hawking, one of the finest minds to have ever lived, once famously said, “AI is likely to be either the best or the worst thing to happen to humanity.” This is of course true, with valid arguments both for and against the proliferation of AI.
As a practitioner, I have witnessed the AI revolution at close quarters as it unfolded at breathtaking pace over the last two decades. My personal view is that there is no clear black and white in this debate. The pros and cons are very contextual – who is developing it, for what application, in what timeframe, towards what end?
It always helps to understand both sides of the debate. So let’s try to take a closer look at what the naysayers say. The most common apprehensions can be clubbed into three main categories:
A. Large-scale Unemployment: This is the most widely acknowledged of all the risks of AI. Technology and machines replacing humans for doing certain types of work isn’t new. We all know about entire professions dwindling, and even disappearing, due to technology. Industrial Revolution too had led to large scale job losses, although many believe that these were eventually compensated for by means of creating new avenues, lowering prices, increasing wages etc.
However, a growing number of economists no longer subscribe to the belief that over a longer term, technology has positive ramifications on overall employment. In fact, multiple studies have predicted large scale job losses due to technological advancements. A 2016 UN report concluded that 75% of jobs in the developing world are expected to be replaced by machines!
Unemployment, particularly at a large scale, is a very perilous thing, often resulting in widespread civil unrest. AI’s potential impact in this area therefore calls for very careful political, sociological and economic thinking, to counter it effectively.
B. Singularity: The concept of Singularity is one of those things that one would have imagined seeing only in the pages of a futuristic Sci-Fi novel. However, in theory, today it is a real possibility. In a nutshell, Singularity refers to that point in human civilization when Artificial Intelligence reaches a tipping point beyond which it evolves into a superintelligence that surpasses human cognitive powers, thereby potentially posing a threat to human existence as we know it today.
While the idea around this explosion of machine intelligence is a very pertinent and widely discussed topic, unlike the case of technology driven unemployment, the concept remains primarily theoretical. There is as yet no consensus amongst experts on whether this tipping point can ever really be reached in reality.
C. Machine Consciousness: Unlike the previous two points, which can be regarded as risks associated with the evolution of AI, the aspect of machine consciousness perhaps is best described as an ethical conundrum. The idea deals with the possibility of implanting human-like consciousness into machines, taking them beyond the realm of ‘thinking’ to that of ‘feeling, emotions and beliefs’.
It’s a complex topic and requires delving into an amalgamation of philosophy, cognitive science and neuroscience. ‘Consciousness’ itself can be interpreted in multiple ways, bringing together a plethora of attributes like self-awareness, cause-effect in mental states, memory, experiences etc. To bring machines to a state of human-like consciousness would entail replicating all the activities that happen at a neural level in a human brain – by no means a meagre task.
If and when this were to be achieved, it would require a paradigm shift in the functioning of the world. Human society, as we know it, will need a major redefinition to incorporate machines with consciousness co-existing with humans. It sounds far-fetched today, but questions such as this need pondering right now, so as to be able to influence the direction in which we move when it comes to AI and machine consciousness, while things are still in the ‘design’ phase so to speak.
While all of the above are pertinent questions, I believe they don’t necessarily outweigh the advantages of AI. Of course, there is a need to address them systematically, control the path of AI development and minimize adverse impact. In my opinion, the greatest and most imminent risk is actually a fourth item, not often taken into consideration, when discussing the pitfalls of AI.
D. Oligarchy: Or to put it differently, the question of control. Due to the very nature of AI – it requires immense investments in technology and science – there are realistically only a handful of organizations (private or government) that can make the leap into taking AI into the mainstream, in a scalable manner, and across a vast array of applications. There is going to be very little room for small upstarts, however smart they might be, to compete at scale against these.
Given the massive aspects of our lives that will likely be steered by AI enabled machines, those who control that ‘intelligence’ will hold immense power over the rest of us. That all familiar phrase ‘with great power, comes great responsibility’ will take a whole new meaning – the organizations and/or individuals that are at the forefront of the generally available AI applications would likely have more power than the most despotic autocrats in history. This is a true and real hazard, aspects of which are already becoming areas of concern in the form of discussions around things like privacy.
In conclusion, AI, like all major transformative events in human history, is certain to have wide reaching ramifications. But with careful forethought these can be addressed. In the short to medium term, the advantages of AI in enhancing our lives, will likely outweigh these risks. Any major conception that touches human lives in a broad manner, if not handled properly, can pose immense danger. The best analogy I can think of is religion – when not channelled appropriately, it probably poses a greater threat than any technological advancement ever could.
Article | March 15, 2021
Microsoft recently announced that it’s leveraging a new global strategic partnership with Telefonica to jointly develop “go-to-market plans for regions the company does business.Last year during Mobile World Congress 2019, Microsoft took the veil off its newfound relationship with the international telecommunications giant, Telefonica.Highlighted during this year’s announcement was Microsoft’s opening of a new datacenter region in Spain. Microsoft’s new data center comes at a time where the company looks to help expedite Spain’s digital transformation.
Article | March 15, 2021
Saurav Singla is a Senior Data Scientist, a Machine Learning Expert, an Author, a Technical Writer, a Data Science Course Creator and Instructor, a Mentor, a Speaker.
While Media 7 has followed Saurav Singla’s story closely, this chat with Saurav was about analytics, his journey as a data scientist, and what he brings to the table with his 15 years of extensive statistical modeling, machine learning, natural language processing, deep learning, and data analytics across Consumer Durable, Retail, Finance, Energy, Human Resource and Healthcare sectors. He has grown multiple businesses in the past and is still a researcher at heart.
In the past, Analytics and Predictive Modeling is predominant in few industries but in current times becoming an eminent part of emerging fields such as health, human resource management, pharma, IoT, and other smart solutions as well.
Saurav had worked in data science since 2003. Over the years, he realized that all the people they had hired — whether they are from business or engineering backgrounds — needed extensive training to be able to perform analytics on real-world business datasets.
He got an opportunity to move to Australia in the year 2003. He joined a retail company Harvey Norman in Australia, working out of their Melbourne office for four years.
After moving back to India, in 2008, he joined one of the verticals of Siemens — one of the few companies in India then using analytics services in-house for eight years.
He is a very passionate believer that the use of data and analytics will dramatically change not only corporations but also our societies. Building and expanding the application of analytics for supply chain, logistics, sales, marketing, finance at Siemens was a very fulfilling and enjoyable experience for him.
Siemens was a tremendously rewarding and enjoyable experience for him. He grew the team from zero to fifteen while he was the data scientist leader. He believes those eight years taught him how to think big, scale organizations using data science.
He has demonstrated success in developing and seamlessly executing plans in complex organizational structures. He has also been recognized for maximizing performance by implementing appropriate project management tools through analysis of details to ensure quality control and understanding of emerging technology.
In the year 2016, he started getting a serious inner push to start thinking about joining a consulting and shifted to a company based out in Delhi NCR.
During his ten-month path with them, he improved the way clients and businesses implement and exploit machine learning in their consumer commitments. As part of that vision, he developed class-defining applications that eliminate tension technologies, processes, and humans. Another main aspect of his plan was to ensure that it was affected in very fast agile cycles. Towards that he was actively innovating on operating and engagement models.
In the year 2017, he moved to London and joined a digital technology company, and assisted in building artificial intelligence and machine learning products for their clients. He aimed to solve problems and transform the costs using technology and machine learning. He was associated with them for 2 years.
At the beginning of the year 2018, he joined Mindrops. He developed advanced machine learning technologies and processes to solve client problems. Mentored the Data Science function and guide them in the development of the solution. He built robust clients Data Science capabilities which can be scalable across multiple business use cases.
Outside work, Saurav associated with Mentoring Club and Revive. He volunteers in his spare time for helping, coaching, and mentoring young people in taking up careers in the data science domain, data practitioners to build high-performing teams and grow the industry. He assists data science enthusiasts to stay motivated and guide them along their career path. He helps fill the knowledge gap and help aspirants understand the core of the industry. He helps aspirants analyze their progress and help them upskill accordingly. He also helps them connect with potential job opportunities with their industry-leading network.
Additionally, in the year 2018, he joined as a mentor in the Transaction Behavioral Intelligence company that accelerates business growth for banks with the use of Artificial Intelligence and Machine Learning enabled products. He is guiding their machine learning engineers with their projects. He is enhancing the capabilities of their AI-driven recommendation engine product.
Saurav is teaching the learners to grasp data science knowledge more engaging way by providing courses on the Udemy marketplace. He has created two courses on Udemy, with over twenty thousand students enrolled in it. He regularly speaks at meetups on data science topics and writes articles on data science topics in major publications such as AI Time Journal, Towards Data Science, Data Science Central, Kdnuggets, Data-Driven Investor, HackerNoon, and Infotech Report. He actively contributes academic research papers in machine learning, deep learning, natural language processing, statistics and artificial intelligence.
His book on Machine Learning for Finance was published by BPB Publications which is Asia's largest publisher of Computer and IT Books. This is possibly one of the biggest milestones of his career.
Saurav turned his passion to make knowledge available for society. Saurav believes sharing knowledge is cool, and he wishes everyone should have that passion for knowledge sharing. That would be his success.
Article | March 15, 2021
Internet of Things, according to congressional research service (CRS) report 2020, is a system of interrelated devices connected to a network and/or to one another, exchanging data without necessarily requiring human to machine interaction.The report cites smart factories, smart home devices, medical monitoring devices, wearable fitness trackers, smart city infrastructures, and vehicular telematics as examples of IoT.