On the surface, it would seem that artificial intelligence (AI) is widespread in the telecom industry. For years we've been familiar with voice-activated menu systems that respond to your verbal commands. However, the potential for AI in the telecom arena goes much deeper than voice controls, albeit with some unique challenges. Scott Matteson: What are the opportunities for AI in the telecom space? Tom Footit: Telecom networks generate an enormous amount of data, and as a result there are a lot of opportunities for AI in this space.

Traditional cybersecurity tools such as mere anti-malware software or login audits aren't going to be sufficient in 2020--additional resources will be needed to protect organizations and their employees from cyberthreats. Artificial intelligence (AI) and machine learning (ML) are making productive inroads in the cybersecurity space. I spoke with Anish Joshi, vice president of technology at AI solutions provider Fusemachines, and Greg Martin, general manager of the Security Business Unit at Sumo Logic, a machine data analytics organization to get their input on the topic. The interviews have been lightly edited. Scott Matteson: What are the common pain points with cybersecurity?

Cybersecurity is increasingly important as more and more attacks happen all the time, leaving organizations scrambling for solutions. How can you keep your company safe from attacks and the resulting financial losses? I discussed the topic with Alex Manea, chief security and privacy officer at Georgian Partners, a North American venture capital firm that invests in growth-stage companies using artificial intelligence and machine learning technologies to solve business problems. SEE: How to become a cybersecurity pro: A cheat sheet (free PDF) (TechRepublic) Scott Matteson: What mistakes are companies making in cybersecurity? Alex Manea: One of the worst things that you can do is to try and stop every single attack, but that's a fairly typical mistake.

AI and ML are often touted as silver bullets, but real-world applications for the technology seem thin on the ground. Artificial intelligence (AI) and Machine Learning (ML) are some of the latest tools being used in the fight against application security vulnerabilities. However, the complexities involved can make it hard to discern what's actually being used and what lives in a fictional Hollywood setting. I spoke to Ilia Kolochenko, CEO of web security company High-Tech Bridge to clear up any confusion. Scott Matteson: What is the overall state of application security today?

Job obsolescence is always a concern for workers, especially in technology fields where automation and artificial intelligence (AI) might endanger human-based positions. Human technical know-how is too valuable an asset to dismiss; at the very least, if one technological role evaporates it will lead to the creation of a new role. I discussed where AI is heading and how the IT workforces can prepare with Terri Schlosser, head of product, technical, and solutions marketing at SUSE, the open source Linux provider. Scott Matteson: What is the current status of AI? Terri Schlosser: While AI has been talked about for many years, it is a market that is just now starting to take off -- doubling every year, with analysts projecting growth from $9.5 billion in 2018 to $118.6 billion in 2025. In fact, AI is helping companies do things like sharpen customer service, organize calendars, verbally respond to questions, automate recruitment processes, and sense when machines need to be repaired. However, there are many areas where the full advantages of AI have not been leveraged, such as in the workplace.

As a system administrator working in technology for more than 25 years, I've provided some measure of IT support. While it's rewarding to help people, sometimes the monotony in doing so can lead to feeling unfulfilled, especially when there are better and more promising tasks afoot--if you can just find time to work on them. It's equally challenging for users and administrators to handle both support ticket platforms and the communication gaps and lags, which can hinder or impede resolutions, plunging both sides into bureaucratic red tape that lowers satisfaction all around as unresolved issues pile up. This is where artificial intelligence (AI) can play a role. AI can help to free up technology pros for more meaningful endeavors in a more streamlined, goal-driven fashion.

Staying ahead of the tide is the mantra for today's technology professionals. As technology and related processes evolve, those who work in the field must update their skills and even careers if necessary. Some traditional help desk, system, and network administrator roles are fading out to be replaced by endeavors requiring a heftier and more diverse skills set. Machine learning (ML) and artificial intelligence (AL) are two such fields making steady inroads into the IT world. People looking for a future career in technology would do well to become familiar with both ML and AI.

Artificial intelligence (AI) and machine learning (ML) are some of the latest tools being used in the fight against application security vulnerabilities. However, the complexities involved can make it hard to discern what's actually being used and what lives in a fictional Hollywood setting. I spoke to Ilia Kolochenko, CEO of web security company High-Tech Bridge to clear up any confusion. Scott Matteson: What is the overall state of application security today? Has it improved in the last 12 months?

Machine learning involves the automation of operations via intelligent mechanisms, which can adjust and adapt as needed. This reduces the need for human intervention--provided the right series of controls are in place. A diverse array of uses and applications exist for machine learning across multiple industries such as manufacturing, healthcare, security, and more. In most cases, machine learning analyzes processes and determines which data or actions are valuable or significant, and which are not. I spoke with email security organization Edgewave's President Steve Kelley about machine learning as it applies to the issue of email phishing, which represents a constant threat to organizations and users.

Machine learning (ML) remains an area of strong investment these days as businesses seek to automate operations via intelligent mechanisms, which can adjust and adapt as needed. This reduces the need for human intervention--provided the right series of controls are in place. However, there is no one-size-fits-all approach to adopting machine learning; most companies and the departments therein approach the concept from different perspectives with an array of various objectives. Some of these objectives are more coherent than others, which produces inefficiencies and unexpected outcomes, and may eventually cause a machine learning shakeout. Before companies take on any machine learning project, they need clear goals so as to establish an effective machine learning strategy which drives real business value.