AI is a priority for governments and businesses worldwide. Poor data quality is a key aspect of AI that has been overlooked. AI algorithms are based on reliable data in order to produce optimal results. However, if the data is incomplete, incorrect, or not sufficient, it can have devastating consequences. Poor data quality can result in adverse outcomes for AI systems that identify patients' diseases. These systems can produce inaccurate diagnoses and predictions, which can lead to misdiagnosis and delayed treatment.

Manasi Vartak is founder and CEO of Verta, a Palo Alto-based provider of solutions for Operational AI and ML Model Management. AI continues to transform our world as companies look to win over consumers with intelligent experiences delivered in real time on smartphones, smart TVs, smart cars--smart everything. But along with new opportunities, organizations are also finding new challenges as they seek to cross the AI chasm. Here are the top six AI/ML trends that I'll be tracking in the year ahead, along with recommendations for how enterprises can stay ahead of each trend. A recent study by our company's research group, Verta Insights, found that more than two-thirds of ML practitioners expect real-time use cases to increase significantly over the next three years.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! More often than not, when organizations deploy applications across hybrid and multicloud environments, they use the open-source Kubernetes container orchestration system. Kubernetes itself helps to schedule and manage distributed virtual compute resources and isn't optimized by default for any one particular type of workload, that's where projects like Kubeflow come into play. For organizations looking to run machine learning (ML) in the cloud, a group of companies including Google, Red Hat and Cisco helped to found the Kubeflow open-source project in 2017.

At the age of eight, Leon Portz was gradually losing his eyesight due to a congenital condition when he was given his first computer. By the age of nine, he had figured out how to speed up the machine-generated...

Artificial Intelligence (AI) has arguably become a household term in modern enterprises. By now, most companies have embraced some type of business initiative that includes AI in their digital transformation. Artificial Intelligence is a broad term, but much current research and development focuses on machine learning (ML), a subdiscipline whereby machines learn from data as opposed to being explicitly programmed. With AI and ML targeting a broad spectrum of enterprise users, IT professionals must develop new skills to succeed in this emerging space. An understanding of the business and its most pressing problems is a transcendent competency for any IT professional.

Companies are planning to increase their spending on artificial intelligence and machine learning as a result of the pandemic, and IT leaders believe that those initiatives should have been a higher priority for their organizations all along, according to a newly released survey by Algorithmia, a provider of ML operations and management platforms. Algorithmia's "2020 Enterprise AI/ML Trends" survey was completed in August by over 100 IT directors and above who are involved with those initiatives and work in companies with at least $1 billion in annual sales and 5,000 or more employees, the company said. There is little doubt the events of the past six-plus months have disrupted the plans of IT organizations. In fact, 42% of IT leaders responding to Algorithmia's survey said that at least half of all their AI/ML projects were impacted from a priority, staffing, or funding standpoint because of the COVID-19 pandemic. SEE: Microsoft's new feature uses AI to make video chat less weird (TechRepublic) But that doesn't mean those projects are going away--instead, their focus may have shifted, Algorithmia said. For example, 54% of IT leaders said their projects were focused on financial analysis and consumer insight prior to the pandemic.

Over the past few months, the COVID-19 virus has had a huge impact on the globe. As of April 28, according to the World Health Organization, there have been more than 2.8 million confirmed cases worldwide and nearly 198,000 confirmed deaths reported in more than 213 nations across the globe. The COVID-19 Pandemic is forcing governments and businesses into actions that are critical in the effort to minimize the rate at which the virus spreads. On March 19th, all residents in California, 40 million people, were asked to "shelter in place" and leave their homes only for basic necessities. Any bay area citizen who has lived through often nightmarish commutes can now travel corridors with ease that a month ago would have been congested with bumper to bumper traffic.

When it comes to artificial intelligence (AI) and machine learning (ML) projects, the biggest challenge for CXOs isn't necessarily deployment, but rather, managing these initiatives. For example, what do you anticipate your AI/ML budget will look like? What business areas are you applying AI/ML in? How knowledgeable is your upper management about AI/ML? Sometimes even determining the manager of managing initiatives can become an issue.

It will take time, but at some point every application will have its share of "AI Inside." Today, however, we're far from that point, and false advertising of AI capabilities isn't helping, something Arvind Narayanan, Associate Professor of Computer Science at Princeton, has called out as "snake oil" in a recent presentation. It's not that there aren't real, useful ways to employ AI today, he stresses, but rather that "Much of what's being sold as'AI' today is snake oil--it does not and cannot work." To help parse good from bad AI advertising, where does Narayanan believe we're making real progress in AI, and where should we myth bust? As with any new technology, aspirations to embrace it always outpace actual production usage, and AI is no different. Even so, according to a Gartner study released earlier in 2019, 59% of enterprises surveyed are using AI today and, of that 59%, they have, on average, four AI/ML projects deployed.

Emerging technologies such as Artificial Intelligence (AI) and machine learning (ML) projects are well underway but concerns remain for CXOs regarding these initiatives. To better understand how enterprises manage their AI/ML projects, Tech Pro Research conducted an online survey in March 2019. According to the survey, 56% of respondents feel implementing AI/ML projects will be more difficult than previous IT projects. The survey asked the following questions: Who is requesting your AI/ML projects? What is your AI/ML project approach?