Artificial intelligence Crash Course

It’s easy to get sucked into the hype around artificial intelligence, but it’s just as easy to get duped into thinking it’s all hype. The truth is somewhere in the middle. AI’s uses come in many forms, from simple AI tools that respond to customer chat to complex machine learning algorithms that predict the trajectory of an entire organisation. Despite years of overpromising, AI is not sentient machines that reason like humans but rather more narrowly-focused pattern matching at scale to complement human reasoning.

In order to help business leaders understand what AI capabilities, how to use artificial intelligence and where to begin an AI journey, it is essential to first dispel the myths surrounding this huge leap in AI technology.?

What is artificial intelligence AI?

It’s easy to imagine AI functioning like science-fiction robots or, closer to reality, fully autonomous self-driving cars. Neither is a reality today, nor will either be a reality in computer science anytime soon. The main difference between AI and ML(Machine Learning) or DL(Deep Learning) is that one is written in a #powerpoint presentation and the other is #Python . We’ll use AI here as a shorthand that includes machine learning and deep learning.

The truth of AI today is much more limited, though it’s still incredibly powerful. The key to appreciating AI is to recognise that it’s largely a pattern-recognition tool that can run at a scale that is dramatically beyond any human, yet never quite replaces humans. Even at its best, AI delivers acceptable though not perfect results, giving people the ability to step in, observe the data and reason therefrom.

AI isn’t truly intelligent in the way we define intelligence: It can’t think and lacks reasoning skills, it doesn’t show preferences or have opinions, and it’s not able to do anything outside of the very narrow scope of its training. Note, however, that AI can and is just as biased as the data we choose to feed into our ML models. In turn, though we rely on ever-increasing quantities of data to make decisions, that data is just as increasingly mediated by machines that try to spoon-feed it to us in ways that make it easier to consume.

What can artificial intelligence do?

Artificial intelligence is essentially pattern matching at scale. No human can comb through gargantuan piles of data to uncover patterns in that data — machines can. By contrast, machines struggle when presented with an outlier that might be easy for a human to spot but contradicts the data the machines have been trained with. Machines can’t reason, but people can. The best artificial intelligence applications are highly focused and combine human reasoning with the brute power of ML.

Since the #covid19 pandemic began in early 2020, artificial intelligence and machine learning has seen a surge of activity as businesses rush to fill holes left by employees forced to work remotely or those who’ve lost jobs due to the financial strain of the pandemic.

The artificial intelligence rich definitely got richer in 2021, according to the 2022 Stanford AI Index report. Private venture investment in AI exploded to $93.5 billion in 2021, more than doubling the 2020 tally. At the same time, the nature of where organisations are focusing their AI investments has changed. The global pandemic shifted AI priorities and applications: Instead of solely focusing on financial analysis and consumer insight, post-pandemic AI projects have trended toward customer experience and cost optimisation, Algorithmia found.

What are the business applications of AI?

An AI system is capable of amazing things, and it’s not hard to imagine what kind of business tasks and problem solving exercises they could be suited to. Think of any routine task, even incredibly complicated ones, and there’s a possibility an AI can do it more accurately and quickly than a human — just don’t expect it to do science fiction-level reasoning.

In the business world, there are plenty of AI applications, but perhaps none is gaining traction as much as business and predictive analytics and its end goal: Prescriptive analytics.

Business analytics is a complicated set of processes that aim to model the present state of a business, predict where it will go if kept on its current trajectory and model potential futures with a given set of changes. Prior to the AI age, such analytics work was slow, cumbersome and limited in scope.

When modeling the past of a business, it’s necessary to account for nearly endless variables, sort through tons of data and include all of it in an analysis that builds a complete picture of the up-to-the-present state of an organisation. Think about the business you’re in and all the things that need to be considered, and then imagine a human trying to calculate all of it — cumbersome, to say the least.

Predicting the future with an established model of the past can be easy enough, but prescriptive analysis, which aims to find the best possible outcome by tweaking an organization’s current course, can be downright impossible without AI help.

Like other AI applications, customer experience and cost optimization are based on pattern recognition. In the case of the former, AI bots can perform many basic customer service tasks, freeing employees up to only address cases that need human intervention. AI like this has been particularly widespread during the pandemic, when workers forced out of call centers put stress on the customer service end of business.

There are many AI software platforms and AI machines designed to do all that heavy lifting, and the results are transforming businesses: What was once out of reach for smaller organisations is now feasible, and businesses of all sizes can make the most of each resource by using AI to design the perfect future.

Analytics may be the rising star of business AI, but it’s hardly the only application of artificial intelligence in the commercial and industrial worlds. Other AI use cases for businesses include the following.

Recruiting and employment

Human beings can often overlook qualified candidates, or candidates can fail to make themselves noticed. Artificial intelligence can streamline recruiting by filtering through larger numbers of candidates more quickly and by noticing qualified people who may go overlooked.

Fraud detection

Artificial intelligence is great at picking up on subtle differences and irregular behaviour. If trained to monitor financial and banking traffic, AI systems can pick up on subtle indicators of fraud that humans may miss.

Cybersecurity

Just as with financial irregularities, artificial intelligence is great at detecting indicators of hacking and other cybersecurity issues.

Data management

Using AI, you can categorise raw data and find relations between items that were previously unknown.

Customer relations

Modern AI-powered chatbots are incredibly good at carrying on conversations thanks to natural language processing. AI chatbots can be a great first line of customer service.

Healthcare

Not only are some AI applications able to detect cancer and other health concerns before doctors, they can also provide feedback on patient care based on long-term records and trends.

Predicting market trends

Much like prescriptive analysis in the business analytics world, AI systems can be trained to predict trends in larger markets, which can lead to businesses getting a jump on emerging trends.

Reducing energy use

Artificial intelligence can streamline energy use in buildings, and even across cities, as well as make better predictions for construction planning, oil and gas drilling, and other energy-centric projects. AI is also being used to minimise corporate water use in the face of climate change.

Marketing

AI systems can be trained to increase the value of marketing both toward individuals and larger markets, helping organisations save money and get better marketing results.

If a problem involves data, there’s a good possibility that AI can help. This list is hardly complete, and new innovations in AI and ML are being made all the time.