/The Machine Learning Future Is Now: How AI is Disrupting Entire Industries

The Machine Learning Future Is Now: How AI is Disrupting Entire Industries

Summary: Machine learning and artificial intelligence (AI) are no longer the concepts of science fiction – they’re a $1.41 billion industry that is already making big changes to the way we understand and use immense databases for a wide range of purposes.

Original author and publication date: Predictive Oncology / Nasdaq – January 4, 2022

Futurizonte Editor’s Note: If AI is no longer science fiction (and it clearly it is not), what other science fiction ideas will soon become reality? Time travel, perhaps?

From the article:

From supporting cutting-edge cancer research to helping businesses track their inventory, machine learning and AI offer the ability to disrupt and enhance our existing processes in virtually every segment of society.

The machine learning market is ready for lift off
The global AI space is expected to grow to $20 billion by 2025, according to research performed by Helomics. And it’s not just AI that offers growth opportunities – it’s also the disruption of long-standing industries that machine learning promises. By enabling business leaders to make more informed decisions, researchers to look at problems in new ways, and offering insights around the clock that no human could possibly contextualize alone, AI is one of humanity’s best allies in the future.

It also carries with it immense market opportunity and the chance to catch the wave of the next big disruption. In fact, 86% of respondents in a 2021 PWC survey said AI technology is now a mainstream part of their company. More than 52% also reported accelerating adoption plans for machine learning and AI technology as a result of the COVID-19 pandemic and its impact on businesses and workplaces worldwide.

How AI and machine learning work
Without getting into the technical weeds, there are a few key capabilities that make machine learning a powerful tool. These include its ability to:

Contextualize vast databases quickly: “Big Data” is now a colloquial term because of how much information is generated, stored, and accessed in virtually every organization on the planet these days. Data is great, but it is only helpful if you can connect various data points and draw conclusions from them. The more data for this, the better…but there’s just one problem: Databases have grown so massive that no human could possibly sit down and parse all that information. It would take an entire lifetime just to scratch the surface of some of these datasets – but for machine learning it’s an easy and ongoing process.

Work around the clock: AI software doesn’t need sleep, so it can analyze data 24/7/365. That means even when your staff goes home, your machine learning algorithms can keep grinding away on current problems and offer up new insights by the time the next shift starts.

Improve and “learns” the more it works: Most machine learning algorithms are designed to improve at what they do as they comb through more data. At Predictive Oncology, for example, our Computational Research Engine (CoRETM) employs a polypharmacological/pharmacogenomic approach which builds a large set of predictive models and selects the optimal treatment plan. Coupled with the Helomics database of 150,000 deidentified patient records, 131 tumor types, and 30 types of cancer around the clock, CoRE is capable of comparing potential drug formulations to known patient responses in live treatment environments, giving cancer researchers and oncologists greater insight into optimal treatments based on every known factor. Best of all, CoRE can begin working with nothing more than two data points: one positive result and one negative result. Instead of relying on assumptions, like some other AI algorithms, CoRE is able to analyze any existing database, including those containing the lab results from Predictive Oncology’s research teams.

READ the full article here