As machine learning becomes more prevalent in business, it has the potential to affect a company’s day-to-day interactions with its customers, whether positively or negatively. Many businesses have already invested heavily in machine learning and have found great success. However, new tech can always be intimidating, scary or potentially difficult to implement.
As we kick off 2021, business technology leaders around the globe are kicking off their 2021 IT strategic plans. If 2020 taught us anything in the technology world, it was that if you weren’t refining your business processes using advances in technology, you should be. Rather than just thinking outside the box, technology leaders should be assuming that there is no box at all.
43% of enterprises say their AI and Machine Learning (ML) initiatives matter “more than we thought,” with one in four saying AI and ML should have been their top priority sooner.
50% of enterprises plan to spend more on AI and ML this year, with 20% saying they will be significantly increasing their budgets.
Machine Learning – The majority of software development teams believe they don’t test well. They understand that the effect of quality defects is substantial, and they invest heavily in quality assurance, but they still aren’t getting the results they want. This is not due to a lack of talent or effort — the technology supporting software testing is simply not effective. The industry has been underserved.
The business intelligence (BI) landscape is changing. Traditionally, BI analysts used dashboarding and analytics tools like Microsoft Excel, Microsoft Power BI and Tableau. However, analytics tools are rapidly evolving, and BI analysts are expected to evolve alongside industry advancements. In particular, predictive analytics used to be in the domain of more technical employees, but today, no-code automated machine learning (AutoML) tools mean anyone can deploy AI.
The ongoing global pandemic has created an urgent need for rapid tests that can diagnose the presence of the SARS-CoV-2 virus, the pathogen that causes COVID-19, and distinguish it from other respiratory viruses. Now, researchers from Japan have demonstrated a new system for single-virion identification of common respiratory pathogens using a machine learning algorithm trained on changes in current across silicon nanopores. This work may lead to fast and accurate screening tests for diseases like COVID-19 and influenza.
By now most of us understand that, in our current era, artificial intelligence (AI) and its subset machine learning (ML) have little to do with human intelligence. AI/ML is all about recognizing patterns in data and automating discrete tasks, from algorithms that flag fraudulent financial transactions to chatbots that answer customer questions. And guess what? IT leaders appreciate the enormous potential.
Machine Learning – One of the challenges in scaling up meat production are issues of disease for the animals. Take bovine respiratory disease (BRD), for example. This contagious infection is responsible for nearly half of all feedlot deaths for cattle every year in North America. The industry’s costs for managing the disease come close to $1 billion annually.
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