Machine intelligence is the combination of AI and ML

Serving dishes, controlling traffic, performing surgeries on humans – think of these and the first impression is that you cannot do without humans here. The situation now seems to have undergone a 360 degree transformation. Gone are the days when every task that you can think of needed human intervention. Now, you find machines taking up the role of waiters, traffic controllers, educators and what not. One of the greatest achievement is in the field of healthcare sector. Machines are assisting doctors and surgeons while performing medical procedures. We have reached a stage wherein some not so difficult procedures are done by machines themselves without the involvement of humans.

This machine intelligence has truly transformed the way we look at things. This kind of intelligence has made it easy to address issues and problems in every field like never before. The reason why machines are intelligent to the extent that they hold the potential to perform tasks just like humans is because of Artificial Intelligence. It is only by virtue of Artificial Intelligence that we get to see human-like machines and computers. This area will see a lot more advancements in the near future, without a doubt. With AI, machines are capable of interacting in an intelligent way. Contrary to popular belief, it is not because of the fact that machines are able to perform a couple of tasks like humans that makes them intelligent. The story goes beyond all of this.

An intelligent machine, system, hardware or any computer is not intelligent because it is able to perform human-like tasks. It is solely because such machines stand the potential to complete tasks in an unreliable environment. Unlike what they are being asked to do, machines are intelligent if they can judge what’s going around by being able to monitor the environment and then acting accordingly. Just imagine how a person would react to different situations. Same is the case with machines. If a person is able to make the right use of intelligence, it is then that he / she is said to be intelligent. If the similar criteria is followed in case of machines and they are able to react just like humans by making the best use of their intelligence, then that is what constitutes an intelligent machine.

Probably the best examples of intelligent machines are Alexa and Siri. Not forgetting to mention here, how popular they have become over a period. Also, their demand continues to rise – thanks to AI. It is impossible to imagine machines being intelligent without Artificial Intelligence in place. It is solely because of AI that the machines can come up with improved decisions for the company. They do this by accessing information in the best manner possible.

What constitutes Machine intelligence?

When talking about machine intelligence, there are two concepts that are critical and form the base of the origin – Artificial Intelligence and machine learning. A combination of these two is the reason why machines are proactive. These two allow the machines to not just collect the data but also process it to arrive at conclusions. Basis these conclusions, the organizations make decisions. To make machines work human-like, naturally, some aspects of humans will have to be incorporated. Skills like problem solving, learning ability, prioritization, etc. go in the making of machine intelligence. Needless to say, programming has to be the pre-requisite. Also, machines are designed keeping in mind the concept of “deductive logic”. Using this, they are well aware of when they have made mistakes. Learning from this, the machines ensure that the same mistake isn’t committed againin the future.

Though not many skills go into making machines intelligent, the way they handle the situations and tackle problems does come out to be surprising.

It is because of this that companies are inclined towards machine intelligence. They include a set of automation techniques and develop a model that’d help them achieve their goals. This form of intelligence has eased a lot of issues and hence will continue to rule for the years to come.

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While biases will always be part of artificial intelligence, is it time for an AI renaissance?

It is not surprising that many industries are turning to artificial intelligence (AI) technologies like machine learning to review vast amounts of data. Be it analyzing financial records to check if one qualifies for a loan or errors in legal contracts or determine if one suffers from schizophrenia; artificial intelligence has got you covered! However, is it totally foolproof or impartial? Can this modern technology be prone to bias like humans? Let us find out!

Bias risks differ for each business, industry, and organization. They can find their way into artificial intelligence systems through numerous ways. For instance, it can either be intentionally introduced into an AI system via a stealth attack or unintentionally, making it hard to ever be seen or discovered. It can also be due to humans who input already biased data that reflects their biased thinking or due to data sampling bias. We also have long tail biases that occur when certain categories are missing from the training data.

It is obvious that presence of bias in data can cause artificial intelligence model to become biased, but what is more dangerous is that the model can actually amplify bias. E.g., a team of researchers found that 67% of images of people cooking were women but the algorithm labeled 84% of the cooks as women. Deep learning (another AI technology) algorithms are increasingly being used to make life-impacting decisions, like in hiring employees, the criminal justice system and health diagnosis. In these scenarios, if the algorithms make incorrect decisions due to AI bias, the results would be devastating in the long run.

For instance, in 2016, Pro Publica, a nonprofit news organization, had critically analyzed risk assessment software powered by AI known as COMPAS. COMPAS has been used to predict the likelihood that a prisoner or accused criminal would commit further crimes if released. It was observed that the false-positive rate (labeled as “high-risk” but did not re-offend) was nearly twice as high for black defendants (error rate of 45%) as for white defendants (error rate of 24%). Apart from this, there are multiple instances where artificial intelligence tools misclassify/mislabeled/misidentified people due to their race, gender, and ethnicity. Like in the same year, when the Beauty.AI website employed AI robots as judges for beauty contests, it found that people with light skin were judged much more attractive than people with dark skin.

It is important to uncover unintentional artificial intelligence bias and align technology tools with diversity, equity and inclusion policies and values in the business domain. As per 2020 PwC AI Predictions 68% of organizations still need to address fairness in the AI systems they develop and deploy.

Often, machine learning, deep learning models are usually built in three phases: training, validation, and testing. Though bias can creep in long before the data is collected and at many other stages of the deep-learning process, bias influences the models in the training phase itself. Generally, parametric algorithms like linear regression, linear discriminant analysis, and logistic regression are prone to high bias. As artificial intelligence systems become more dependent on deep learning and machine learning, owing to their usefulness, tackling AI biases can get more tricky.

While the biases are being addressed in an accelerated manner, the key challenges lie in defining the bias itself. This is because what may sound bias to one developer or data scientist may not mean bias for another. Another concern is what guidelines should ‘fairness’ adhere to – is there any technical way to define fairness in artificial intelligence models? Also, it is important to note that varying explanations will create confusion and cannot be satisfied every time. Further, it is crucial to determine what shall be error rates and accuracy for different subgroups in a dataset. Next, data scientists, need to factor in the social context. If a machine learning model works perfectly in criminal justice scenarios, it does not imply it will be suitable for screening candidates for a job position. Hence social context matters!

No doubt that opting for diverse data can alleviate AI biases, by giving space for more data touchpoints and indicators that cater to different priorities and insights, it is not enough. Meanwhile, the presence of proxies for specific groups, make it hard to build a deep learning or any other AI model that is aware of all potential sources of bias.

Lastly, not all AI biases have a negative footprint nor influence. In such cases, explainable AI (XAI) can help to discern whether a model uses good bias or bad bias to make a decision. It also tells us which factors are more important when the model makes any decision. Though it will not eliminate biases, it will surely enable human users to understand, appropriately trust and effectively manage AI systems.

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