Artificial Intelligence and Machine Learning
Artificial Intelligence and Machine Learning—What they are, how they work together, and what they mean for your business
This spring, Fortune Business Insights reported, “The global machine learning (ML) market is expected to grow from $21.17 billion in 2022 to $209.91 billion by 2029, at a CAGR of 38.8% in forecast period.”
But for many of us the terms Artificial Intelligence (AI) and Machine Learning (ML) still mainly call to mind the science fiction narratives we grew up on—perhaps The Jetsons or 2001: A Space Odyssey in the 1960s, Electric Dreams in the 1980s, or Her in 2013—all of which posited a future filled with human-like computers, and provided visions that ranged from comic to tragic, sublime to disturbing, transcendent to out-of-control.
Our modern world may not have the sleek Technicolor gleam or outerspace living arrangements that Hollywood imagined over many decades, but the reality is that we now use AI and ML every day in life and work, and their capabilities are exploding.
Given the vast mythology (and understandable skepticism) that surround AI and ML, it’s important—especially for enterprise leaders—to understand what these concepts and technologies actually are, the differences between them, and what they can and cannot do.
AI and ML are related, and work together, but have discrete functions.
Artificial Intelligence refers to the capability of a computer program to simulate human thinking and behavior, and to independently perform tasks, make decisions, and solve problems based on this thinking, which would otherwise require human logic, experience, and expertise.
Machine Learning is a subset and application of AI, in which a computer learns automatically and, without being directly programmed to do so, continually improves upon its learning as it encounters more and more data. ML is the way AI gains its intelligence.
Further, Deep Learning, which is a subcategory of ML, powers the most human-like AI processes, in which a computer can understand and process even unstructured data like natural language. It is based on a complex web of algorithms, called a neural net that mirrors the structure of the human brain.
AI and ML are similar in several ways. They both rely on enormous quantities of data, are capable of producing vital, actionable insights into that data, making useful predictions and forecasts that can inform decision-making.
In our daily lives, AI powers GPS maps, chatbots, and autocorrect/text edit, allows facial ID to unlock your phone, and much more.
Machine Learning is the technology that refines your search results, curates your social media feed, and operates voice-activated personal assistants like Siri and Google Home, which gather and distill hyper-personalized information based on your past interactions, offering results progressively more tailored to your preferences. ML predicts traffic patterns and the price of an Uber. It filters spam and malware on your devices.
AI and ML work together to enable a rather mind boggling range of functions, opportunities, and benefits across diverse industries including finance and banking, healthcare, retail, customer service, sales and marketing, food and beverage, automotive, transportation, manufacturing, energy management, cybersecurity, and more.
Predictive analytics help businesses decipher large data sets to better understand customer behavior, discover patterns, predict trends, and get out ahead of competitors.
Recommendation engines allow companies to suggest products that might interest their customers based on data analysis.
Speech recognition, which allows a computer to identify spoken words, works in conjunction with Natural Language Processing (NLP) and Natural Language Understanding (NLU) which, respectively, enable computers to understand not only written and verbal human language, but also the meaning of sentences, and to translate it into data. This can help businesses improve customer experience by discerning what they’re saying and the sentiment behind it.
Internet of Things (IoT) devices and sensors can detect problems before they happen, and teach machines to self-calibrate.
Further, AI and ML offer companies a broader range of data sources, both structured and unstructured to tap into, supporting faster, stronger, more evidence-based decisions. Automation of various repetitive processes reduces human error, increases efficiency, and reduces costs while freeing your team to focus on more meaningful tasks that need the human touch.
But despite their extraordinary promise and possibility, AI and ML are not the solution to every problem—they have certain limitations.
Though machines can learn and make predictions through data and mathematical models, true reasoning is distinctly human. There are cases in which neural networks are less efficient than the human mind because even when processing vast amounts of data, machines may lack other necessary forms of knowledge and context. Also, accurate ML relies on enormous quantities of data and can be impeded by a lack thereof, or a lack of good data. Further, training in one set of data does not necessarily translate across wider sets, and can therefore produce flawed results. Scalability can be an issue, due to the narrowness of algorithms for specific use; human intervention is often required as data expands.
And of course—it’s not just the rogue machines of science fiction—there are real and pressing ethical concerns about becoming too trusting of artificial intelligence, particularly letting it outweigh human intellect, judgment, and experience. Or how machine learning might skew our perception of information. Or the dangers of misapplication of data. And in a sort of catch-22, the regulatory restrictions that protect against abuses of data almost unavoidably inhibit the use of the broader data sets that are needed to train machines more effectively.
Amidst all the hype, there’s a lot to sort through.
When you’re stymied or overwhelmed by the pace of technological advancement and what it means for your company, there are solutions. Lukasa’s team of experts take a partnership approach to every project, working side by side with enterprise leaders and their teams to cut through the noise and implement the most advantageous Artificial Intelligence and Machine Learning tools for each unique business.
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