Future of Machine Learning (AI)
Machine Learning is the process of letting the computer become smarter by using inputs. User inputs or mass collected datasets are trained in such a way that the computer can relate to them. Machines can do so many simultaneous tasks in a glimpse that takes humans days or months.
Now as we talk, the world is suffering from Covid-19. Supercomputers can do 200 quadrillion or 200 petaflops calculations per second. To host this much power, it takes two tennis court size of server space. And this is just one supercomputer.
At the TensorFlow World 2019 keynote, Jeff Dean (Google’s senior engineer) talked about computer vision. The error rate of humans in computer vision is now 5 percent while computers are down to 3%, according to research done by his team. While it can be more than a number, the outcome is huge.
The process of ML is pretty simple to put into plain words. Lots of data is fed to the computer. A beginner who just got onto the learning process of learning ML can experiment with datasets in Kaggle. Writing codes for it can be done in regular browsers in localhost. These data are divided into two main categories as test data and train data. The training data is used to inform the computer to find resembling information. Just so you know, they are pretty good at it now.
Replicating expertise with computer vision has come a long way. Using Artificial Intelligence (AI) to diverse as what to use, what to dump, and what to learn from is now being used in everyday tasks. Like a smartphone camera can automatically detect what light setting to choose from just by the time and lighting condition passing through it. It was programmed in this way.
Neural Architectural search, Transfer learning, Multitask learning for small data are expanding rapidly with the increase of rapid computing power.
Amazon, Google, NVidia, DLabs are some of the top researchers of Machine Learning (AI) programs. We can now predict a little future data by comparing past data alongside.
Quantum computing is boosting ML research with greater flexibility and it is helpful for the current time.
According to Amazon Web Services (AWS), businesses can index lots of customer data from their feed with simple clicks.
If you’ve been on the internet for long, it is easily noticeable to find more accurate search result and search suggestion than it was before. AI starts its calculation around the text, voice, or image and suggests the most fitting result even before hitting the enter button.
TensorFlow is a great place to find models as they are mostly collected from top tech platforms of the globe. TensorFlow Extended (TFX) enables end to end pipeline to use on mobile devices. It can easily run in a mobile browser or node.js platform.
Enthusiasts are keen to learn ML (AI) today as it is one of the most interesting fields to jump into, that directly impacts the world.