Many Artificial Intelligence (Ai) myths and misconceptions make it hard to uncover the truth. How do you know facts from science fiction?
Let’s talk about three Ai myths we are asked about all the time:
It’s likely you’ve heard of these myths as they are constantly talked about. At ElectrifAi, we believe Ai encompasses more than just deep learning, Ai does contain bias and it’s important to prepare for that, and Ai doesn’t replace jobs but enhances the ingenuity of employees.
Deep learning can achieve state-of-the-art or sometimes above human performance in certain areas, such as computer vision, gaming, voice recognition, etc. Deep learning is also known as deep neural learning or deep neural network because it mimics the workings of the human brain in processing data and finding patterns.
Deep learning, however, often requires massive and expensive labeled data and compute power. Model inference latency of deep learning models is higher than XGBoost[1]or traditional regression algorithms.
Machine learning and deep learning are both important parts of Ai and both have their uses. Machine learning uses algorithms to sift through tons of data and learns from the data to make decisions. Deep learning also uses algorithms but structures them in layers that provide different interpretations to the data. Such deep layers are what create the massive part of deep learning.
Deep learning is a part of ElectrifAi’s machine learning offerings; but the difference is our machine learning models are nimble and cost-effective. We are always finding ways to make them even more efficient for our clients.
Check out this press release to learn about our collaboration with Inspur that does just that by providing powerful processing power at a fraction of the cost.
Ai currently has bias. Most of the bias is unconscious, however, as prejudiced assumptions can be made while creating the algorithms or even from the training data itself.
The question is, can we trust the judgment of Ai?
Well, trust is earned. ElectrifAi has had over 2,000 engagements with Fortune 500 clients who trust us to provide pre-built machine learning models that solve real business problems.
We do our best to keep our models bias-free, but it is impossible to be completely unbiased. What you must do is recognize that which could be biased and find a product.
Cognitive biases, which are feelings towards a person or a group of people, can enter a machine learning model algorithm by data scientists who are unaware they are introducing a bias or using a training data set that already includes biases.
Machine learning models can also form biases from a lack of complete data. The data could include a specific group of people and not represent the whole population.
Recognizing biases can be difficult to do on one’s own. Experienced data scientists at a firm specializing in machine learning with rigorous quality control checks in place is the best way to ensure as little bias enters the model as possible.
We delve more into this topic in our white paper, Leading from the Top: Enterprise Ai in 2021. We spoke with 10 highly experienced executives from top companies who give their thoughts around the benefits and drawbacks of implementing Ai. You can read this to help you make informed decisions about your Ai journey.
Ai certainly makes mundane and repetitive tasks less of a chore and dreaded task; but it doesn’t replace jobs – it makes jobs more effective.
For example, imagine not having to sift through hundreds of thousands of images to locate specific objects. Applied Ai with computer vision can process those images for you and alert you when it locates the object, allowing you to act upon that information right away. Computers also “see” more than the human eye can because it scans every single pixel in a fraction of the time it would take a human and computers don’t get tired eyes.
We cover Applied Ai in Video Analytics in episode 3 of our engaging and live weekly series, This Week in Applied Ai.
Another example is processing invoices. Normally, accountants manually process PDFs and handwritten invoices for hours each day. As each invoice starts to look the same, even the brightest accountant can misread information or forget to check the invoice against the work order and it goes through.
Millions of dollars in invoice fraud happen every day. Imagine having the ability to not only save your employees the headache of scanning invoices all day but to save money from accidental or intentional fraud.
Machine learning isn’t replacing that accountant. It is increasing the ingenuity of that accountant and making them even more efficient; more invoices can be processed each day and fewer mistakes are made.
Plus, it makes the ability to audit invoices even better. From being able to audit 10% of invoices at random, the machine locates anomalies and flags them for review. Now the auditor can review invoices that really need a closer look versus taking a sample and hoping to find something to save the company money.
Employees are happier and more fulfilled at their work by not having to do mundane and repetitive processes. Machine learning doesn’t take away jobs. It helps make those people who have the jobs much more efficient.
Want to learn more about how machine learning can help prevent invoice fraud? This Week in Applied Ai, Episode 5 takes a deep dive into that topic.
At the heart of ElectrifAi’s mission is a commitment to making Ai and machine learning more understandable, practical, and profitable for businesses and industries across the globe. We hope this blog has dispelled some myths and misconceptions around Ai and machine learning and enlightened you with the practical applications possible.
We would love to explore all the possibilities our pre-built machine learning models can do for your business. Contact us today for a custom demo!
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[1] Learn more about XGBoost here: https://xgboost.readthedocs.io/en/latest/
