We've been overcomplicating machine learning for years. Sometimes we confuse it with the over-hyped artificial intelligence, talking about replacing humans with robotic reasoning when really ML is about augmenting human intelligence with advanced pattern recognition. Or we burrow into deep learning when more basic SQL queries would get the job done. But perhaps the greatest problem with ML today is how incredibly complicated we make the tooling because, as Confetti AI co-founder Mihail Eric has posited, the ML "tooling landscape with constantly shifting responsibilities and new lines in the sand is especially hardest for newcomers to the field," making it "a pretty rough time to be taking your first steps into MLOps." Normally we look to tooling to make tech easier.
ZDNet spoke to iQuanti CEO Sastry Rachakonda about why its new tool is so effective. Rachakonda: We are a digital marketing company but we work across channels and primarily in the B2B space. That's our core strength but we work across various campaign management areas. A lot of the search we focus on relates to work and analytics but we also set up and manage a lot of sites for people. Fundamentally, we are about using Google search data to drive more effective marketing.
Announcing the fifth release candidate for the Linux kernel version 4.14, Linus Torvalds has revealed that fuzzing is producing a steady stream of security fixes. Fuzzing involves stress testing a system by generating random code to induce errors, which in turn may help identify potential security flaws. Fuzzing is helping software developers catch bugs before shipping software to users. Google uses a variety of fuzzing tools to find bugs in its and other vendors' software. Microsoft has launched the Project Springfield fuzzing service to allow enterprise customers to test their own software.
Because contributions to DAOs can come from anywhere, tools that qualify and quantify different types of contributions (e.g. In addition to monetary compensation, DAOs can leverage reputation-building tools to motivate value-aligned participants to take on more ownership and grow with the DAO. Decentralisation should not come at the expense of efficiency in the long run. Progressive decentralisation enables an initial team to search for product market fit on its way toward credible neutrality. We will explore examples of DAOs doing this through constrained delegation and working groups, as well as the tools that provide additional layers of checks and balances that hold executors accountable to token holders.