With the speed of change in artificial intelligence (AI) and big data, podcasts are an excellent way to stay up-to-date on recent developments, new innovations, and gain exposure to experts’ personal opinions, regardless if they can be proven scientifically. Great examples of the thought-provoking topics that are perfect for a podcast’s longer-form, conversational format include the road to AGI, AI ethics and safety, and the technology’s overall impact on society.

‍As children we believed in magic, imagined, and a fantasy where robots would one day follow our commands, undertaking our most meager tasks and even help with our homework at the push of a button! But sadly it always seemed that these beliefs, along with the idea of self-driven aero cars and jetpacks, belonged in a future beyond our imagination or in a Hollywood Sci-fi. Would we ever get to experience the future in our lifetime?

There’s an AI-led developer and operations (DevOps) evolution afoot which is stoking SREs’ increasingly critical efforts to assure and improve the customer experience by automating the toil out of observability. This movement feeds on a supercharged process of turning telemetry into actionable insight by automatically drawing anomalies, changes and events out of the full-stack event and telemetry data, and analyzing it for correlation and causality.

Fake news spreads six times faster than factual news. The algorithms built into social media software, in an effort to hold our attention, feed us information that triggers the basest instincts of our complex psychologies. These are some of the starkest realities exposed in the Netflix documentary “The Social Dilemma.” Holding our attention is what the robot (if you will) was programmed to do. And it’s doing its job really, really well.

Let me share with you my story about finding the right AI Solution to improve our support self service. The starting point of my journey was pretty common, many will read this and think “Yes! I suffer the same pain”. At JFrog we produce tons of relevant and clever documentation. It’s all out there, available. And yet, we struggle to get to the right piece of information when looking for something specific.

With the promise of unprecedented potential, artificial intelligence (AI) and predictive analytics have permeated into every field of business. Due to their ability to help retail staff serve customers better, personalize video recommendations based on users’ preferences, reduce employee churn, and detect fraud and security threats, AI and predictive analysis are rapidly being adapted across industry verticals.

As your tech stack increases, every new device (network devices, servers, applications) creates a large amount of distributed log data. This forms part of what is called “machine data”, which is growing 50x faster than traditional business data. In fact, everything in your stack is continuously writing new events to your log files, including error logs that contain a record of critical errors encountered by a running application, operating system, or server.

In 1927, the world was introduced to the origins of Artificial Intelligence (AI) in the form of a robot in the movie Metropolis. Throughout nearly a century since then, movies have continued to iterate on the complexities of AI, as both a fun take on it and serious commentary on the potential concerns and consequences. This is all well and good, but as AI has continued to evolve, we find ourselves asking, “how can we actually use this to make our lives easier?”