Back in the 1950s, the fathers of Artificial Intelligence McCarthy and Minsky described AI as any task performed by a machine or a program that, if a human carried out the same task, we would say the human had to apply intelligence to accomplish the task. Since then, we have come a long way into this field. Artificial Intelligence is dramatically altering the field of computing, from chips to software to systems. The rise of machine learning – especially deep learning – has been possible thanks to the increasing use of GPUs, and soon, it will take over the majority of the world’s computing activity. Deep learning is pushing at the boundaries of what today’s computers can do. It is the key to training neural networks, the more compute-intensive function. Not only do the models keep getting bigger and expensive to train but the pace of demand for compute cycles is also increasing sharply. Whereas the pace of chip improvement has hit a wall, and its performance is only increasing by 3% per year. These chips and their architecture must change drastically in order to get more performance out of transistors. Giants like Intel and Nvidia have already made improvements, but companies invested in Machine Learning think it’s not enough. Google, Facebook, and even Amazon are working on their own custom chips based on different assumptions. Experts say the next wave of chips and software will have vast amounts of memory and neural networks which will be dynamically constructed. Artificial Intelligence is one of the most rapidly growing fields today where AI talent training is on the rise. Although it is highly rewarded and invested in but despite the shortage of AI talent continues to be a major hurdle to the wide adoption of the technology across the industry. To reduce such stress there is now a thing called AutoML This is an Automated Machine Learning for those non-data scientists to do simple AI, and even for those trained data scientists to do complex work faster. This technology is still catching on but this could very well be one day become a pathway for speedy work on AI. With the emergence of automated machine learning, this training process is getting easier. The companies like DataRobot specializes in AutoML whereas the companies like Microsoft Azure, Amazon Web Services, and Google Cloud Platform allows some free automated machine learning experience. There are open source AutoML frameworks as well such as Auto-Keras, Auto-sklearn, and even Uber’s recently launched open-sourced platform called Ludwig. The number of jobs increased by AI has increased 3 times than it took away last year and the funds invested in Artificial Intelligence also grew by almost 80% exceeding to a whopping $27 billion per year; with North America escorting the way at 55% of the market share. Some of the applications that AI presents are autonomous vehicles, demand forecasting, healthcare, robotics (mainly in logistics and manufacturing), Robotic Process Automation (RPA), and text analysis. Robotics Process Automation has been an overnight enterprise success which was 15 years in the making. RPA appears to deliver a lot of benefits to its enterprises, such as increased operational nimbleness and reduced operating costs which helps to compete with the newcomers. Autonomous Vehicles or AV is another high-profile area of application and why won’t it be; self-driving cars are a multi-billion-dollar industry! Companies like Waymo, Ford, Cruise, and Uber have spent so much to make this case. According to a report, an average Californian drives 14.435 miles per year, where only 11 out of 63 companies have driven more than that. Waymo’s self-driving service drove more than one million miles last year, which is nearly three times as much as the second best GM Cruise. Tesla, on the other hand, didn’t report their metrics but it is known that Tesla has more on this than any other in this game and is ahead in this race. Tesla also designs their own AI chips which power the computer on board; which is another innovative area as it is piloting the capabilities of AI. With Reference to the State of AI Report 2019 co-authors Nathan Benaich and Ian Hogarth: Nathan Benaich, the founder of Air Street Capital and RAAIS, feels that this is the perfect time to make novel chips for the reasons of inference and training of the AI models. “We think this is true because of industry adoption of AI models for several large-scale use cases, especially in consumer internet. As a result, chip designers have a clear customer to design for. Designing chips, however, is an endeavour that is very capital intensive and requires significant domain experience that can only be acquired over many many years.” Benaich also said that this is actually linked to geopolitics, as the companies building these types of AI startups will definitely attract a lot of capital investment. “When it comes to ‘deep tech’ (for example, semiconductors), the US (along with other key countries like South Korea and the UK) remains dominant. This means that China remains heavily dependent on imports for these kinds of technologies. Indeed, China spends seven-times more money on importing semiconductors than it does selling them for export.” In the report published by Benaich and Hogarth, they included predictions of a merger of more than $5 billion! While it hasn’t yet been practically come out, but some authors are backing these predictions. During a speech by China’s president, Xi Jinping, at the beginning of last year, something really stood out; it was a bookshelf. That bookshelf contained two books on AI which raises the question of why were they there? Looking at it from the perspective of a similar incident of Russia “accidentally” aired the designs for a new weapon, maybe those books weren’t actually an accident and maybe China was sending a message. Perhaps, China has been operating in an Americanized world and it is turning to AI to escape. Recently, the founder of Air Street Capital pointed out that the Chinese tech ecosystem is actually growing remarkably. “Of particular note is the ecosystem’s focus on nurturing the growth of AI-first technology companies. By recent counts, China is home to the largest number of AI startups valued over $1 billion. The pace with which these AI startups acquire scale is arguably second to none in the world. With regards to fundamental research progress, we can consider a) the number of papers accepted into leading academic research conferences, b) the citation count of these papers, and c) the international ranking of universities for related courses such as computer science and engineering. Looking at the 1st and 2nd measure, China’s contribution to global AI research output is on an upswing. For the third measure, we can see that US and European universities still account for the overwhelming constituency of the top 20 institutions in global rankings. Having said that, Tsinghua University and Peking University are both in the top 20 for computer science and engineering courses.” However, it isn’t totally based on the Chinese prospect and since Benaich and Hogarth are actually based inside Europe, their perspective on that is also of interest. “We are in a period of incredible transformation. The economy is changing. Governance is in flux. And the only way we can tackle our toughest societal challenges is with the help of powerful technologies such as AI — workable, safe, ethical AI. That is where Europe’s unique strengths lie, at the fulcrum between China and America’s AI rivalry.” The founder of RAAIS believes that in the past decade the European Industry has thrived and flourished in process and hence a new ecosystem is emerging that is both sustainable and sophisticated in terms of finance. “This will have a major impact on Europe and Britain’s AI fortunes for years to come. The context is important. At a time of Brexit and a US-China trade war, everyone wonders what Europe’s — and in particular, the UK’s — role will be in the global economy. Some count it out. Others argue that it will be a leader in ethical business, leveraging the EU’s tough privacy rules implemented last year. But the reality will probably be different: Britain looks set to be the AI R&D lab of the world. In the past, the main driver was the excellent universities like Oxbridge, Imperial and UCL. They trained the talent that now works at leading US technology companies. But now there’s much more happening. In the last 18 months, US technology companies have made deep inroads into the UK ecosystem to strengthen their AI products.” The Days Of Future Are Upon Us: With such advancements in Artificial Intelligence, we’re ever so near to a massive breakthrough. Back in the 1950s, this concept of AI arose and now we’re at a stage with Automated Machine Learning and open sources like Ludwig, and even Microsoft and Google giving developers a platform to come with a revolution. The stakes have never been higher than it is today; the vision of Artificial Intelligence finally moving forward into a positive direction.