Artificial Intelligence (AI) and Machine Learning (ML) are being embraced by greater numbers of individuals, businesses, and governments as rising efficiency and productivity are permitting exponential growth in certain sectors of the global economy. However, the gap in efficiency and productivity between those sectors and businesses benefitting from AI and ML versus those that have not is also growing exponentially. This risks leaving those at the bottom further and further behind with less and less chance of catching up with the leaders.
Embracing and integrating Artificial Intelligence (AI) is a challenge for any organization, but for large companies the culture change required to implement AI is often daunting. What some organizations may view as an essential means of remaining competitive, streamlining production processes, and cutting costs, others may view as part of a larger organizational transformation process, meant to reinvent a company. Some, of course, view it as both. There are as many potential permutations associated with embracing AI in the manufacturing process as there are potential applications for doing so.
As artificial intelligence (AI) becomes better at performing narrow tasks traditionally done by humans, how will governments create policies to protect peoples’ livelihoods? Should it be up to governments to do so or will it create a new generation of entrepreneurs? Will AI create a new breed of welfare recipients or will it spur governments to dramatically reform welfare? Some have argued that, while some jobs will be displaced, new jobs will be created in their place.
A new book titled A.I. Supremacy: Winning in the Era of Machine Learning reveals that the pace of progress and change is occurring at exponential rates each year and is becoming so great that many in AI and ML community believe that in as little as a couple of decades from now human level AI will no longer be the realm of fantasy.
The two countries that appear to be the best positioned to leap forward in the coming decade are China and South Korea. Both are light years ahead of the competition.
When Artificial Intelligence (AI) and Machine Learning are combined with the interconnectedness of global supply chains, they provide a range of unprecedented opportunities and potential perils for international businesses. On one hand, rising efficiency and productivity is permitting exponential growth in some sectors and businesses. On the other hand, the gap in efficiency and productivity between those sectors and businesses that have embraced AI and Machine Learning versus those that have not is also growing exponentially, leaving those at the bottom further and further behind.
The cyber era heralded unparalleled opportunities for the advancement of science, technology and communication, and unleashed a range of new attack vectors for rogue elements, criminals and virtual terrorists. The era of machine learning is doing much the same, for the promise of advancement has gone hand in hand with a range of new perils and an expanded set of actors capable of carrying out attacks using artificial intelligence (AI) and machine learning systems. This flows naturally from the efficiency, scalability and ease of diffusion of AI systems, which can increase the number of actors who can carry out attacks against civilian, business and military targets.
Browse through anything about Artificial Intelligence and machine learning and chances are, you will run into two types of articles: First, you will find all the thought pieces by the likes of the Big 4 accountancy firms, major consultancies, the World Economic Forum and others that discuss all the opportunities that AI provides. Second, you will find very technical articles for the “techies” that focus on the ins and outs of these technologies. What you will struggle to find are pieces and conversations about the key risks and related implications these technologies create with a broader audience in mind. Until now. Today, we talk AI Supremacy.
There are a number of potential applications for using AI in the legal domain, especially for those that relate to the automation of repetitive and routine tasks. Conducting legal research can be tedious, monotonous and time-consuming, but performing timely and comprehensive legal research is critically important for lawyers. AI systems certainly aid lawyers by performing legal research on relevant case law and applicable statutes faster and more thoroughly than most lawyers may be able to do on their own. Such systems are proving powerful enough to use data to predict the outcome of litigation and enable lawyers to provide more impactful advice to their clients in connection with dispute resolution issues.