News & Events
Future of Machine Learning
- November 20, 2020
- Posted by: elanwp
- Category: Data Science Blogs
Machine learning is a trendy topic in this age of Artificial Intelligence. The fields of computer vision and Natural Language Processing (NLP) are making breakthroughs that no one could’ve predicted. We see both of them in our lives more and more, facial recognition in your smartphones, language translation software, self-driving cars and so on. What might seem sci-fi is becoming a reality, and it is only a matter of time before we attain Artificial General Intelligence.
If we’re being technical, machine learning has actually been around since the 1950s, when Arthur Samuel coined the term at IBM. Early statistical models in those days paved the way for today’s modern artificial intelligence. On the contrary, while today’s machine learning is miles ahead of what it used to be, there’s always room for improvement.
With advancements in algorithms, statistical modeling, and computing, machine learning will only get more efficient. And while it’s not always easy to predict what this efficiency will look like, some experts have an idea.
1. AI Is Helping Combat COVID-19
A World Health Organization report from February 2020 revealed AI and big data are playing an important role in helping healthcare professionals respond to the coronavirus (COVID-19) outbreak in China. So, how is AI and machine learning helping combat COVID-19? There are many applications, including:
Thermal cameras and similar technologies are being used to read temperatures before individuals enter busy places like public transport systems, government buildings, and other important areas. In Singapore, one hospital is leveraging KroniKare’s technology to provide on-the-go temperature checks using smartphones and thermal sensors.
2. ML Framework Competition
In 2019, one of the key trends in the ML was PyTorch vs. TensorFlow competition. During 2019, TensorFlow 2 arrived with Keras integrated and eager execution default mode. PyTorch eventually overtook TensorFlow as the framework of choice for AI research. Why is PyTorch better for research? PyTorch integrates easily with the rest of Python. And it is simple and easy to use, making it accessible without requiring too much effort to set it up. In contrast, TensorFlow crippled itself by repeatedly switching APIs, making it more difficult to use.
3. AI Analysis for Business Forecasts
ML-based time series analysis is a hot AI trend in 2020. This technique collectively analyzes a series of data over time. When used correctly, it aggregates data and analyzes it in such a way that allows managers to easily make decisions based on their data.
Using an ML network to process the complex calculations required to apply statistical models to your business’s structured data is a major improvement over traditional methods.
4. Reinforcement Learning
Reinforcement learning (RL) is leading to something big in 2020. RL is a specialized application of deep learning that uses its own experiences to improve itself, and it’s effective to the point that it may be the future of AI.
When it comes to reinforcement learning AI, the algorithm learns by doing. Initially, actions are tried at random, but eventually, this becomes a logical process as it attempts to attain specific goals. The operator rewards or punishes these actions, and the results are fed back into the network to “teach” the AI.
5. AI-driven Biometric Security Solutions
Significant advancements have been made in biometric verification. Bio-ID is no longer something you’d expect to see in sci-fi films. This emerging ML trend is one to keep your eye on. ML’s efficient approach to gathering, processing, and analyzing large data sets can improve the performance of your biometric systems. Running an efficient biometrics system is all about performing matching tasks quickly and accurately, and this is a task that ML networks excel at.
6. Automated Machine Learning
AutoML is adapted to execute tedious modeling tasks that once required weeks or months of work by professional data scientists. AutoML runs systematic processes on the raw input data to choose the model that makes the most sense. AutoML’s job is to find a pattern in the input data and decide what model is best applied to it. Previously these activities were processed by hand.
The next leap in design productivity for semiconductor chips and the systems built around them will come from the fusion of fully integrated EDA computational software tool flows, the application of distributed and multi-core computing on a broader scale and ML/DL. The current wave of artificial intelligence (AI) and ML innovation began with improved GPU computing capacity and the smart engineers who figured out how to harness it to accelerate deep neural network training. AI/ML will play a key role in the design of next-generation platforms, enabling the proliferation of today’s technology drivers including 5G, hyperscale computing and others.
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