The implications of “Artificial Intelligence” and “Machine Learning” across multiple industries are creating a great amount of hype nowadays. In today’s era of digital innovation, numerous organizations and entrepreneurs have adopted AI/ML technology so that their businesses may gain the ability to predict customer preferences, avoid human error, improve decision making, boost productivity, streamline routine tasks, and much more.
Machine learning has even transformed our workplaces and our personal lives in many ways, like robotics, AI assistant devices in homes, smart appliances, online shopping recommendations, social media collaborative suggestions, and self-driving cars.
Recent statistics says that about thirty-seven percent (37%) of organizations have implemented artificial intelligence in various forms to boost their chances of success.
Amir Husain, the founder and CEO of company SparkCognition, once said:
“Artificial intelligence is kind of the second coming of software. It’s a form of software that makes decisions on its own, that’s able to act even in situations not foreseen by the programmers. AI has a wider latitude of decision-making ability as opposed to traditional software.”
This raises a question: is it beneficial to infuse any product with AI? To answer this and determine whether your product actually needs AI, a few factors need to be considered before integrating AI into a business product.
These can also help in analyzing the needs of AI for any potential application.
Before taking a step toward building AI in a business product, the organization should conduct research and consultation with their technology partners to identify if the precise problem at hand needs AI technology to work better. Blindly investing money into an AI solution which cannot streamline the problem, is not a wise business decision.
Once verified, the next hunt is to make sure that the selected AI approach is the best choice and can solve the problem effectively.
While designing a product, teamwork counts, and time management is valuable. One important factor to consider is whether integrating an AI solution into a current project would help to eradicate the recurring tasks of your team or whether it would require more input from your team.
If the newly added AI tool/solution requires extra work from your team, then you should definitely first evaluate the value that the end product adds to your business, and if it's worth the tiresome work by your team to change the existing product.
Having said that, one more key point is to verify whether the AI solution is a standalone application that you want to deploy, or whether it is to be integrated with the existing clockwork. Integrating it with the existing application or as part of the product can significantly disrupt the team's progress, and business ROI can be impacted.
After selecting a suitable AI tool, apply it to a small sample of your data. Installations can be hectic sometimes, so make sure that it is easy to use, and that a team member is available to install it in product as needed in a feasible amount of time.
The best strategy is to start with a small part, get it running, test it for any flaws, collect feedback, and then expand the process to rest of the data. Do not throw all of your data into the AI at once.
Digitally innovative AI tools are different from traditional software and thus may require training and a set of guidelines for end users and clients that allow them to have a user-friendly connection with the product.
AI tools sometimes work on large amounts of complex data to optimize the workflow for modeling and computing objectives. So, consider the storage requirements (servers, data warehouses, etc.) before attempting to implement complex AI algorithms.
Security factors needs to be considered while infusing new AI solutions into your product or even whole business functions. When dealing with an organization with large amounts of sensitive data, proper measures should be taken to avoid any mishaps or potential threats. AI algorithms accessing sensitive/personal information and performing irreversible decisions can sometimes be dangerous.
Machine Learning is a subset of AI that processes complex troves of data and implements algorithms to learn to make predictions and decisions over time.
This is being used in a wide variety of applications in different forms, so it is vital to analyze your data and have defined set of requirements before implementing AI/ML techniques. AI/ML do not lend themselves solving every problem and thus can sometimes be harmful to your product.
Albert Einstein once said:
“If I were given one hour to save the planet, I would spend 59 minutes defining the problem and one minute resolving it.”
This means you ensure that your scope is properly determined before implementing a solution. One harmful impact that AI/ML can have on your product is “Algorithm Error”, which refers to the fact that no AI/ML algorithm is one hundred percent accurate. Although they are generally very accurate, errors happen, and major problems can arise when algorithms make bad decisions on important issues.
For example, in March 2018, a self-driving Uber car killed a pedestrian in Arizona. This is a dramatic example of how machinery failure can affect humanity at higher and unacceptable level.
Another unexpected harmful impact of AI is the possibility that it is “Economically Unstable”. Artificial Intelligence and Machine Learning come with a cost for installation, maintenance, and upgrades. If there is a breakdown of the system, then a huge procurement cost and time is also required to fix it, which is not economically feasible for any business.
Another risk is “Security Threats”. Current AI is not sophisticated enough to link its decisions to the inputs that produced them, or to explain the choices it makes.
These algorithms are sometimes inscrutable and can brutally affect people by their unpredictable decisions. For example, there was a medical incident in which a Medicaid AI program in Arkansas suddenly cut caretaker hours for people with heavy disabilities without any valid reason, and both people affected by the decision and assessors using the tools were unable to understand why.
Security in terms of “Access to sensitive information” is also a risk, as AI algorithms use your data and personal information to act wisely and make decisions based on the information they have access to. For a large organization, this amount of confidential data access can lead to privacy invasion at the hands of the machine.
The term “No improvement with time” refers to the fact that the same AI approaches that solve one problem more accurately and faster than the human brain also have a limited scope of working within dynamic environments. They are unable to respond according to altered situations and have limitations in growth or improvement over time.
Everything comes with its own pros and cons, and so is the case with AI/ML technology. Without it, we could not have developed smart phones and digitized gadgets, which are assisting people in the modern age.
But given the presence of such a vast variety of AI tools, solutions, and algorithms, the tough task is to select/prioritize the established AI/ML approach which you think can do a better job for you than others. Selecting the right set of AI tools, combinations of libraries, models, and algorithms depends highly on your situation and domain.
At Crowdbotics, we use AI to comb through the universe of open-source software components and intelligently select the best code packages for your app build. Want to learn more? Get in touch with us today.
August 10, 2020