Artificial intelligence (A.I.) has been cited as a reason for reducing head count in companies, leading to fears that it will replace jobs. However, A.I. can complement human labor rather than replace it, and can be used to deliver “expertise on tap” in jobs such as healthcare delivery, software development, law, and skilled repair. Workers can focus on different tasks, and new technology can lead to new jobs. How technology makers design, business leaders use, and policymakers regulate A.I. tools will determine how generative A.I. ultimately affects jobs.

As AI and machine learning become more prevalent in various industries, they also introduce new security risks. Here are seven of the most common data security threats facing AI/ML systems: model poisoning, data privacy, data tampering, insider threats, deliberate attacks, mass adoption, and AI-driven attacks. To protect themselves from these threats, companies must implement strict access management policies, have data protection policies in place, conduct regular security audits, and use security solutions that provide ultra-secure confidential computing environments.

Enterprises can transform their data into knowledge accessible through a voice AI knowledge assistant, which allows employees to retrieve data with greater ease and speed. Voice technology as a means of interacting with digital technology and data is a reliable, fast, and convenient method. The voice AI-powered knowledge assistants function by capturing and processing inputs and returning useful outputs, making an organization's knowledge base searchable in a natural way.

Synthetic data, which is computer-generated artificial data based on user-specified parameters, can be a solution to the cold start problem faced by small and medium businesses (SMBs) when adopting AI. Synthetic data is cheaper and less time-consuming to process than authentic data, and studies suggest that it produces the same results as real data. Synthetic data can be used for developing prototypes, ensuring data privacy, testing and training for unprecedented scenarios, promoting data flexibility, and exploring niche applications. However, its limitations, such as the potential for unintentional bias and the need to combine it with real-world data in applications that face safety issues, must be considered.

The shift towards inductive reasoning in AI poses a dilemma as it relies on probabilistic-based models that are more influenced by observations, creating a potential for overlooked anomalies. Researchers tend to dismiss outliers as noise, which can lead to p-hacking, where data is manipulated to produce statistically significant results. Analyzing anomalies could lead to preventing catastrophic events like airplane crashes. The sheer volume of data available today has made humans rely more on algorithms to make sense of it, but this reliance could lead to overlooking important anomalies.