Concept of AI and its adoption
Even though the concept of AI (Artificial Intelligence) has been emerged around a few centuries ago, it wasn’t well-acknowledged till 1950s, when it’s true potential was explored in front of the world. Although, a generation of philosophers, mathematicians, and scientists were familiar with the idea of AI, well known British Polymath – Alan Turing (later recognized as the Father of Artificial Intelligence) proposed that if humans are capable of utilizing the available productive information to make crucial decisions and solve important issues, then why can’t the machines do the similar thing?
Despite the fact that Turing drafted the proposal regarding how to test the capabilities and intelligence of machines in his seminal paper, “Computing Machinery and Intelligence” in the year of 1950, however, his findings were overlooked by his associates and did not advance for further investigations. The existing computer’s limited capabilities and restricted funding might be the key reasons for this remissness.
During the early 1970s, computers witnessed robust growth as they became faster, affordable, and highly capable of storing more data. Although, a number of experiments with the machines demonstrated the promise of problem-solving and spoken language’s interpretation skills, yet it took a considerable amount of time well before the machines could able to think, recognize, and attain the level of natural language and blockchain technology processing.
Back in the 1980s, an AI research team came into the limelight by bringing up abundant funds and advanced algorithmic tools such as “deep learning” applications which enabled the computers to learn by utilizing the experiences. At the same time, some of the most prominent experts brought the systems which imitated the human’s decision-making process. However, during the 2000s AI observed a tremendous amount of acknowledgment as against the lack of public attention and sufficient funds.
Over these years, AI has been considered as a geeky thing that is mostly loved computer-associated people and misunderstood by every other person outside the tech community. Neverthlessness today, AI has been turned out as hottest thing outside of the tech world by extending its arms across each and every sector of the business world.
However, “AI must be kept in its proper perspective,” said Hilary Mason, General Manager – Machine Learning, Cloudera. AI to attain its fullest potential, “we have to make it boring! We have to say AI is not something that we’re excited about; AI is just one tool it’s just as exciting as your C compiler”, she added.
As AI is ubiquitous i.e. capable of running in the backdrop of multiple systems as well as applications, the building of new AI-oriented enterprise technology might assist to tackle the issues associated with its smooth integration into several fundamental business processes. “So when I say let’s make it boring, I actually think that’s what makes it more exciting”, she remarked.
Further, emphasizing about the significance of AI, Mason claimed that AI is not a recreation of human intelligence; however, it is simply a set of computer programs built on the basis of available information and is capable of expanding its expertise as per the requirements. By observing the previous evidences associated with AI, one can easily make out why it captured the tremendous importance by the enterprises right from mid-scale to large-size in the world of tech.
As per Mason, a great number of innovations associated with AI, especially in the prospect of machine learning, take place in the field of academics or maybe in startups. Generally, the academics focus on fresh ideas and/or techniques that will meet the standards of well-defined benchmarks to get their research papers published, instead of focusing on the real tasks that will assist businesses to build efficient production systems to solve the critical issues. Those ideas lack the capability of tackling the real-time issues related to the production processes with respect to scalability and repeatability.
Likewise, as the startups are extremely constrained in terms of resources, including capital funds, domain expertise, vast database, and brief experience, they show higher reluctance towards experimenting with AI. Under these circumstances, the large-scale enterprises operating complex-natured businesses are the only ones which are capable of investing large amounts of resources allied with capital, human, technical expertise, and an enormous amount of data. Normally, generation of this huge data is a result of an enterprise’s intricate processes across the business.
The following are some of the key advice given by the Mason for effective incorporation of AI applications into the business processes.
- Gather distinctive ideas from various processes of the business for the future project and then validate their practicability on the basis of varied aspects such as technicality, affordability, legality, scheduling, and operational constraints. These ideas could be carried across different departments of the organization and/or brought from the external sources from other organizations as well as other industries.
- Prior to the integration of AI into the business processes, assess all the financial indicators associated with the organization. Identify the substantial changes that might emerge with the implementation of an AI. In previous days, owing to its GPU’s, storage costs, and computing costs, AI implementation was regarded as highly expensive. However, nowadays, most of the advanced open-source components of AI are available in the market along with customizable solutions. It implies that, with the help of these components, the organizations can leverage the benefits of AI without building the vital infrastructure physically.
As a matter of fact, it’s the right time to make the AI as boring as much as possible. We must focus on the ultimate outcome of the AI applications, instead of talking too much about its initial results. Let make AI boring and unlock its fullest potential.