A few years back, it’s tough to have any serious discussion regarding Reality of Artificial Intelligence outside of the academic institutions.
But the scenario is different nowadays, almost everyone is talking about AI. Even more, everyone is sharing great ideas with enthusiasm and curiosity. But there are a lot of fake promises, and misleading opinions are also raising.
AI adoption rate and enhanced development in academics had lead to the faster growth of AI than ever expected and accelerated by the deep conviction.
Our biological limits are becoming a significant obstacle to the creation of intelligent systems and machines that collaborate with us to better utilize our biological, cognitive capabilities to achieve more ambitious goals.
To solve real-world problems and create smarter machines, the investment of AI technologies had increased with overwhelming demand.
Many obstacles of AI is cleared over the last few years in academics. Now the significant challenges faced by AI is its adoption into real-world industries. The barriers to the development of AI is misunderstanding and myths.
It is a great challenge for industry leaders to distinguish between the myths and facts of AI.
The reason behind this is noisy and crowded enthusiastic, service providers, and platform vendors. The truth of the AI will be endured once the dust of its gets settled and the winners and losers will be declared eventually. The major challenge is how the industry leaders will have a realistic opinion regarding what the AI can do and can’t do.
This clarification about the AI will give the right ways to solve real-world problems and transforming businesses. This leads to the continuous operation of the facts.AI practitioners have a responsibility to get out of their bubble and call on industry experts to further develop the academic foundations of AI to make adoption in the real world faster, more rewarding and more effective. Responsible.
The mess of AI adoption in industries
All the business leaders from a few years are trying to understand how the AI will be beneficial for their business. Most implementations of AI-based solutions have not gone beyond proof of concepts (POC) in the form of machine learning (ML) dispersed algorithms with limited scope. Many opportunities and resources of the companies are getting wasted with this level of approach to AI adoption.
Simple statistical methods for adding the classification capabilities or some simple predictions in many PoC projects for the analytical solutions can be called as AI solutions. Human intervention is still needed for understanding while deciding for the outcome by the analytics.
The operational conditions and business process are changing continuously, the continual change in the business factors, and newly generated data are reducing perception level can lead to dangerous decisions.
The current approach of trying to incorporate some Machine Learning algorithms into certain areas of activity to gain quick wins is itself a risk and could lead to a decline in industry adoption of AI.
Which would trigger another “AI winter” this time on the industry side? The academic side. The application of even mature AI technologies in this way can add some values, but also a new “artificial stupidity” dangerous to the organization, with catastrophic consequences.
AI systems can’t be biased
As we use human-generated data based on rules we have created to train machine learning algorithms, this data will directly reflect our thinking and approach. These data will determine the behavior of each algorithm.
This creates another misunderstanding that the problem of AI bias is irrelevant in such cases, leaving many people believing, wrongly, that the algorithms are not biased. In such cases, many companies do not know that ML algorithms can represent a high risk and even a legal burden for organizations.
The ethics, accountability, and governance of AI systems are one of the most critical roles of leadership in the AI era. They must invest proactively to inform, guide, and raise awareness throughout the organization.
We should develop new methods and tools to expose biases using appropriate human and machine reasoning based on relevant business and technical knowledge.
Overhyped promises from data
Recent years have shown that, in many cases, companies lack sufficient historical data in the quality and quantity required for current anti-money laundering approaches.
We need to invest considerable effort in different areas such as data engineering, data analysis, feature engineering, feature selection, predictive modeling, model selection, and verification before having algorithms initial.
Predictive analytics solutions use simple statistical models to predict something based on available historical data. This assumes that the future will follow the past and straightforwardly. An assumption that in many cases, proved wrong.
Some of the myths and reality of AI
AI Myth 1: AI is new
Reality: Under the banner of the ‘Intelligence Machines,’ we hear a lot about the robots which takes over humans soon. There are many articles on the internet which have been published that claim that smart, intelligent, and automated systems which will take over all sorts of tasks that humans do today.
For all the current hypes, beliefs and misconceptions, Artificial Intelligence or ai solution is not new into the market as of now.
The idea of bringing non-living objects to life as intelligent beings have been around for quite a time.
In today’s scenario, we can see a wide range of applications of AI in the real world scenario, which makes the business processes efficient and smarter. So, Artificial Intelligence is not so new.
AI Myth 2: AI and Machine Learning, these both are the same things
Reality: Artificial Intelligence is often misinterpreted for machine learning, as are cognitive processing, deep learning, and natural language processing. Differentiable programming is a part of the AI wherein we regularly train an algorithm by feeding the data so that the algorithm can improve and adjust itself.
AI is a broad term that involves machines that can perform the tasks with human-like intelligence, for example, learning, sounds, recognizing objects, understanding language, solving complex problems.
AI Myth 3: AI will keep on giving results as we keep on implementing it
Reality: As we just discussed above, a content stream and a wide range of data are required to enable a product to deliver the Artificial Intelligence. It is just like a human body.
it needs proper training to flourish. As we ingest more data, it starts responding and perceiving in a more humanized manner.
AI Myth 4: It’s about advanced complex algorithms and complex mathematics
Reality: For the machine learning algorithms, you need to use the differentiable programming, algorithms, and mathematics, but AI is data play.
Even if AI has existed for the past several years, the sudden burst of data has facilitated the advancement and increasing applications of the AI in the real world scenario.
As AI receives accurate and updated data, it even continues to mature and also help a product to learn how human feel and think.
AI Myth 5: AI lacks human-like empathy
Reality: AI is meant to take over the repetitive routine tasks which are error-prone and time-consuming so that humans can keep on focus on critical areas where capabilities like the more creativity and problem solving are required.
On the other hand, humans carry some of the unique characteristics like empathy and judgment, which can certainly not to be expected from the robots as a present.
AI Myth 6: AI has human characteristics
Reality: Ai is not like a human brain yet! To make a product think, learn, and understand on its own and empathize with the user, developers have to use the large bodies of data, advanced analytics, and unique algorithms.
However, it takes a massive amount of data and time to learn and have characteristics like humans; Moreover, technologies are still not more capable than humans.
So far, as your goal behind implementing Artificial Intelligence is clear, it is even worth investing in AI since using its business intelligence provided by the algorithms and data, it can replicate the actions and decisions of humans.
AI Myth 7: AI algorithms can magically make sense of any of your unstructured data.
Reality: It is not “load and go,” and the data quality plays an essential role than the algorithm.
The essential input for an AI tool is data – not just any data, but the right sort of data. That means information that is much more relevant to the problems being solved and much more specific to a set of knowledge, skills and a domain knowledge.
Many in the technology industry are erroneously keep on claiming that an AI solution can just be pointed at the data and that the powerful machine learning algorithms will produce the right answer. The Term is “load and go” where all the data is being integrated into the system.
The problem with this segment lies in the vast landscape of codified and explicit enterprise knowledge. AI cannot make data that is too much hard or even has not been processed in a structural way that makes it digestible by today’s system.
At the time of IBM, researchers were developing Watson to play Jeopardy; and at that time, they found that loading specific information sources affecting negativity performance.
Rather than ingest everything and anything, an AI system requires the content and information that has been carefully adapted and is of high quality. Bad data offers terrible results, no matter what the system is. An algorithm is a program, and the application needs useful data.
When a system is using, “machine learning algorithms,” the program arrives at an answer with the continuous approximations and learning is the best way to get to that answer with the use of adjustments to how it processes that data. Having the right data is much essential than algorithms.
AI Myth 8: You need data scientists, machine learning experts, and huge budgets to use AI for the business.
Reality: Many of the tools are increasingly available to business users as intercultural dialogue and don’t even require the Google sized investments.
Some types of AI applications do require some of the heavy liftings by the computer linguists. Moreover, a growing number of software tools that use Artificial Intelligence are becoming more accessible and efficient for business users.
AI technology at one end requires some of the deep expertise in the programming language and sophisticated techniques. Most organizations will opt to leverage the business applications, which is developed on top of tools that companies such as Facebook, Amazon, Apple, Google, and well-funded startups build.
“Training” an Artificial Intelligence is a somewhat mysterious concept that is much more time shrouded in the technical language and even considered a task only for the data scientists.
For some application such as the chatbots to support the customer service, the information used to train the AI systems is frequently the same information that calls centers associates to need to do their jobs.
The primary role of the technical staff is to connect the AI modules and integrate with the existing corporate systems. Some other specialists are also involved in the process.
AI Myth 9: “Cognitive AI” technologies can understand all types of problem in the way the human brain can.
Reality: Cognitive Technologies cant able to solve the issues they were not designed to solve. So, called cognitive technologies can address the type of the problems that typically human interpretation and judgment, which standard programming approaches cannot explain.
These issues include the use of image recognition, execution of complex tasks, the ambiguous language where precise conditions and outcomes cannot be predicted.
AI Myth 10: Machine learning with the help of “neural nets” which means that computers can learn the way according to the humans learn.
Reality: Neural nets are robust, but a long way from achieving the complexity of the human brain or mimicking human capabilities.
One of the most exciting and best approaches to powering the AI is the use of deep learning, which is even built on so-called artificial neural networks.
This design even allows the computer chips to emulate the way biological neurons learn to recognize the patterns. The approach is most of the time used to address several challenges, from improving language translation to speech recognition, image recognition, self-driving cars, and fraud identification.