There's no quicker way to lose a user or buyer of your software than to lose their trust. The software didn't save my data. The website is down frequently. Data integrity is a challenge every company storing data faces. Machine learning SaaS startups face another trust risk – one introduced by probability.
Artificial Intelligence Software has become an integral part of business software and is projected to continue dominating the software market in the foreseeable future. AI software incorporates machine learning (ML) and deep learning into its functionality with an aim to better automate user tasks, save time & energy, make jobs simpler, and boost productivity. Companies can derive the latest technological benefits from cloud-based AI. AI systems provide a wide range of benefits to businesses, such as personalized marketing, customer service, operational automation, inventory management, and recruitment. Nowadays, many AI apps are designed specifically for cloud-based systems, making them quick and easy to deploy.
Economists worry that artificial intelligence could make many workers obsolete. Now, AI researchers could be putting their own jobs at risk. Researchers at Google and elsewhere are writing AI software that's better than humans at writing AI software. Researchers at the Google Brain artificial intelligence research group tasked software with designing a machine-learning system to take a test that benchmarks language-processing software. The software-written software beat previously published results for software designed by humans, according to a report this week on the MIT Technology Review.
Kubeflow brings together all the most popular tools for machine learning, starting with JupyterHub and Tensorflow, in a standardised workflow running on Kubernetes. Optimised on a wide range of hardware and cloud infrastructure, Kubeflow lets your data scientists focus on the pieces that matter to the business. It is an extensible framework, which allows you to leverage the tools of your choice. Start with Tensorflow and JupyterHub or bring your own frameworks and tools. Combined with Kubeflow's automation, this will accelerate your machine learning activities -- from model development to model training to model sharing.
In this blog we will see Machine learning techniques that can be used to perform effective fuzzing on a software system. This system will be integrated with CloudFuzz. CloudFuzz is an integrated software framework for security based fuzzing. The end goal is to provide a workflow that will allow continuous fuzzing and generate reports of the software security vulnerabilities by analysing crashes on a given piece of software. In CloudFuzz we provide crafted data to a software system and analyse the system for crashes. Ultimate aim of fuzzing is to discover bugs and security vulnerabilities in the target software. Probability of discovering a bug increases with the magnitude of code covered by the input provided to target software. Generating inputs with high code coverage is a tricky task. Here is one of the attempts to solve this problem using machine learning.