Drexel University has recently launched a new Artificial Intelligence and Machine Learning (AI/ML) learning group. This initiative is designed to help students, faculty, and staff gain the skills necessary to develop AI/ML applications for research and industry. The goal of this program is to create an environment where people can come together to learn about the latest advancements in AI/ML technology while also gaining hands-on experience with real-world projects. The Drexel AI/ML learning group will be led by Dr. Yaser Abu-Mostafa, Professor of Electrical Engineering at Caltech, who brings decades of experience in machine learning research and teaching. He will be joined by other experts from academia as well as industry professionals who have expertise in various aspects of artificial intelligence and machine learning development.

Technology advancement is the result of human labor. However, developing concepts in artificial intelligence, automation, and machine learning has done a significant amount of the job for us. Changes in customer service, workflows, and business procedures open up new possibilities, deal with outdated habits, and ultimately pave the way for a more secure and confident future. The banking and financial industry is a fantastic illustration of how businesses may adapt to contemporary concepts. This article will look at the relationship between machine learning and FinTech, its motivations in the industry, and its potential applications.

WIRE)--Verta, the Operational AI company, today released findings from the 2023 AI/ML Investment Priorities study, which surveyed more than 460 AI and machine learning (ML) practitioners to benchmark AI/ML spending plans across industry sectors in light of evolving technology trends, industry developments, and macroeconomic conditions. The study was conducted by Verta Insights, the research practice of Verta Inc., and found that nearly two-thirds of organizations are planning to either increase or maintain their spending on AI/ML technology and infrastructure despite economic headwinds in the broader market. "We currently are experiencing an inflection point for the AI/ML industry, with technologies like ChatGPT and Stable Diffusion driving heightened interest in how companies can leverage machine learning models to significantly automate human-based activities with very innovative and game-changing capabilities. Findings from our research study confirm that organizations are continuing to make significant investments in AI/ML technology and talent, despite turbulence in the market, as they orient their business strategies around creating intelligent experiences for their customers," said Conrado Silva Miranda, Chief Technology Officer of Verta. In the research study, 31% of respondents said that their organizations would increase AI/ML spending in 2023 due to the current economic conditions, while 32% said that they would maintain 2022 spending levels for AI/ML technology and infrastructure.

Join us on November 9 to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers at the Low-Code/No-Code Summit. Almost every confidential investment memorandum (CIM) for a tech-driven enterprise includes the company's mention of artificial intelligence (AI) or machine learning (ML) capabilities. But as with other investment buzzwords -- such as "subscription revenue" -- there is a tendency to use AI or ML to suggest complex, business-enabling, proprietary technology and processes to distinguish the offering as differentiated or technologically superior. This is often to garner higher valuation. We've all heard examples of AI failures that make for good headlines and provide interesting cautionary tales.

Democratization of technology and the pandemic have fueled adoption of AI/ML technologies across the public sector. Several public health agencies have leveraged AI/ML technologies to harness the power of data driven intelligence to transform several aspects of community healthcare including the identification of vulnerable populations, patient engagement, optimization of care quality, delivery of personalized interventions, and elimination of fraudulent transactions. While these AI-enabled initiatives have generated new insights and enabled the agencies to improve outcomes, they have also raised concerns regarding the ethical principles and values in AI/ML adoption. There is a renewed focus on ensuring trust, fairness, privacy, accountability, and transparency throughout experimentation to industrialization of AI initiatives. Governance is a critical aspect of AI/ML adoption.

The global artificial intelligence in healthcare market size was valued at USD 10.4 billion in 2021 is expected to expand at a compound annual growth rate (CAGR) of 38.4% from 2022 to 2030. The growing datasets of patient health-related digital information, increasing demand for personalized medicine, and the rising demand for reducing care expenses are some of the major driving forces of the market growth. The growing global geriatric population, changing lifestyles, rising prevalence of chronic diseases has contributed to the surge in demand for diagnosing and improved understanding of diseases in their initial stages. Artificial Intelligence (AI) and machine learning (ML) algorithms are being widely adopted and integrated into healthcare systems to accurately predict diseases in their early stage based on historical health datasets. Furthermore, deep learning technologies, predictive analytics, content analytics, and Natural Language Processing (NLP) tools are enabling care professionals to diagnose patients' underlying health conditions at an earlier stage. The Covid-19 pandemic positively influenced the demand for AI technologies and unearthed the potential held by these advanced technologies.

Cyber-attacks are a big business, as highlighted by recent headlines of ransomware attacks on the Colonial Pipeline and on the Taiwanese computer manufacturer Acer, which allegedly paid a ransom of £50 million. According to Harvard Business Review, the total amount of ransom companies paid to hackers grew by 300% over the period of last 12 months – and high-profile victims of cyberattacks are now forking out millions to survive. It's easy to see why small and medium businesses (SMBs) may feel that cyberattacks are reserved for large enterprises with sky-high revenues – but this assumption comes at a cost. With 49% of SMBs impacted each month by cyber-attacks, it's clear all organisations are targets for cyber criminals. Cyberattacks have evolved in complexity amid the challenges of managing remote or hybrid workforces, meaning it's now crucial that businesses improve their understanding of cybersecurity and invest in strong backup and disaster recovery solutions.

In 1950, Alan Turing published a paper exploring whether or not machines could think. Through this research, he developed “The Turing Test” — a framework that assumes the ultimate bar for “Artificial Intelligence” is whether a machine can exhibit behavior indistinguishable from a human in a given activity. Turing’s research sparked a wave of interest in machine intelligence and subfields of machine learning and deep learning. These disciplines are comprised of AI algorithms that create expert systems to make predictions or classifications based on data. Decades later, these expert systems have shaped our modern definition of the consumer experience. At the same time, they remain largely out of reach for the average user.

Deep Instinct Threat Research team extensively monitored attack volumes and types and then extrapolated their findings to predict where the future of cybersecurity is heading, determine what motivates attackers, and most importantly, lays out the steps organizations can take now in order to protect themselves in the future. One of the most pronounced takeaways from this research on 2021 threat trends is that bad actors are becoming more successful at evading AI/ML technologies, prompting organizations to redouble efforts in the innovation race. Specific attack vectors have grown substantially, including a 170% rise in the use of Office droppers along with a 125% uptick in all threat types combined. The volume of all malware types is substantially higher versus pre-pandemic. In addition, threat actors have made a discernable shift away from older programming languages, such as C and C, in favor of newer languages, such as Python and Go.

To increase their value in the fast-growing AI field, top Artificial Intelligence professionals will need to develop a few key skills that go beyond just technical expertise. According to'LinkedIn Jobs on the Rise: 15 opportunities that are in demand and hiring now', artificial intelligence (AI) is one of the fastest-growing occupations, with practitioners in great demand in 2021. The best AI/ML professionals and teams are well-rounded in their broad business understanding and ability to communicate, in addition to having expertise in Python, C, or Java and an aptitude for math. The next step of digital transformation is organization-wide adoption of AI/ML technologies; therefore a strong team of developers, programmers, and data scientists is essential for enhancing AI literacy from the top down. It is critical for IT leaders to emphasize that AI/ML is intended to improve, not completely replace the organization's teams.