In order to make educated decisions in this fast-moving field, all managers should have a basic understanding of AI. Here are four key facts that will give you an edge.
AI systems learn from the data and feedback that they receive in response to their earlier decisions. Their predictions and actions are only as good as the data they have been trained on. This characteristic makes AI systems very different from traditional deduction-based programming. A traditional program processes data but does not learn from it.Core learning algorithms range from a few to several hundred lines of code. Basic AI is easy to learn – which is one reason why recent progress has been so rapid. You do not need to be a computer scientist to develop an intuitive understanding of AI. The complexity comes in applying AI to real-world problems.
Electronic signals travel about a million times faster than chemical signals in the brain do, which helps AI swallow substantial data and learn and act quickly. When microseconds matter, as they do in some electronic markets, AI can be the only realistic option for participants and regulators.
Among the most important recent breakthroughs in AI are machines’ abilities to interact with humans, access human knowledge, and physically navigate the real world. Although these skills remain imperfect, they are already used in many situations—and they continue to improve quickly.
AI can handle both linear problems (essential generalizations of straight lines) and nonlinear problems (everything else). This two-fold ability opens up a multitude of optimization opportunities in fields such as logistics, production and energy efficiency.
AI architecture combines centralization and decentralization. For instance, self-driving cars drive autonomously, but they transmit their data to a central data centre. The system then uses aggregated data from each car in the fleet to foster central-system learning, and the cars receive periodic software updates based on such learning from the central system. Although they rely on similar heuristics, such as trial and error, machines and humans solve tasks in different ways. The goal in business is to solve a problem, not to create robots that mimic the way humans would perform a particular job. Just as engineers do not design cars to move the way a horse does, autonomous driving should not emulate the actions of human drivers.
Many companies do not understand the importance of data and training for AI success. Frequently, better data is more crucial to building an intelligent system than better-naked algorithms are, much as nurture often outweighs nature in human beings. It is essential for companies to invest heavily in skill upgradation through Data Science Boot Camps for their employees. Students should also go ahead and enrol in Data Science Certification Courses during their Bachelor’s Degree to get ahead of the rest provided by AILABS situated in Sector V, Kolkata.
Efforts to optimize interactions between humans and machines have evolved well beyond training humans to use static computer programs. Augmenting human performance with AI and vice versa—introducing a human into the loop of algorithmic problem solving—are increasingly common and complementary challenges.
Sure AI is now at a place where it is yielding results and its applications are innumerable.
However, the path toward the real and practical application of AI and deep learning remains unclear for many organizations. Business and technology leaders are searching for clarity. Where do I start? How can I train my teams to perform this work? How do I avoid the pitfalls?
The post above lays out the challenges one can expect to face when they dive into this arena