Photo by Alexander Sinn
As the need for additional AI applications grows, businesses will need to invest in technologies that help them accelerate the data science process. However:
Implementing and optimizing machine learning models is only part of the data science challenge.
In fact, the vast majority of the work that data scientists must perform is often associated with the tasks that preceded the selection and optimization of ML models such as feature engineering — the heart of data science. Here are some trends to AI to watch for in 2020:
1. AI and Better Healthcare
AI will practically change the way patients are being treated. AI can perform many tasks in less time and at a fraction of the cost and simplifies the lives of patients, doctors, and hospital management. The power of MRI, X-ray machines, and CT scanners can not be disputed. Robot doctors are gradually taking over the surgery scene by making surgeries cleaner and more precise. AI is already able to detect skin cancer
more accurately than dermatology experts so disease detection will be more accurate by imparting AI.
2. Quantum Computing will Surcharge AI
Another trend to watch in 2020 will be advancements in quantum computing and AI
. Quantum computing promises to revolutionize
many aspects of computer science and could supercharge AI in the future. It is set to dramatically improve the speed and efficiency of how we generate, store, and analyze enormous amounts of data. This could have enormous potential for big data, machine learning, and AI cognition. By massively increasing the speed of sifting through and making sense of huge data sets, AI and humanity should benefit greatly. It may even trigger a new kind of Industrial Revolution; only time will tell.
3. Computer Graphics to Benefit Greatly from AI
One trend to watch in 2020 will be advancements in the use of AI in computer-generated graphics. This is especially true for more photorealistic effects like creating high fidelity environments, vehicles, and characters in films and games. Creating on screen a realistic copy of metal, the dull gloss of wood or the skin of a grape is normally a very time-consuming process. It also tends to need a lot of experience and patience for a human artist. Various researchers are already developing new methods of helping to make AI do the heavy work. AI is being used to improve things like ray tracing and rasterization to create a cheaper and quicker method of rendering hyperrealistic graphics in computer games. Some researchers in Vienna
are also working on methods of partially, or fully, automating the process under the supervision of an artist. The use of neural networks and machine learning to take prompts from a creator to generate sample images for their approval.
4. AI and Deepfakes Becoming More Real
Deepfakes is another area that has seen massive advancement in recent years. 2019 saw a plethora of deepfakes that went viral on many social media networks. But this technology will only get more sophisticated as time goes by. This opens the door for some very worrying repercussions for potentially damaging, or destroying, people’s reputation in the real world. With deepfakes becoming very hard to distinguish from a real recording, how will we be able to tell if they fake or not in the future? This is very important as deepfakes could readily be used to spread political misinformation, corporate sabotage or even cyberbullying. Google and Facebook have been attempting to overcome this by releasing thousands of deepfake videos to teach AI’s how to detect them. Unfortunately, it seems even they are stumped at times
5. AI and Automated Machine Learning (AutoML)
Data preprocessing, transformation AutoML will most probably become more popular in 2020 with the ability to perform ETL tasks. AutoML techniques can automatically do the model selection, hyperparameters optimization and scoring, while different cloud provider already offers an “autopilot” alternative to their services.
6. AI and Federated Machine Learning
Still back in 2017 Google introduced the concept of Distributed Learning, an approach in which models are partially or totally trained using decentralized data. Think about training a baseline model on your machine, then the model is shipped to the final user, which has access to data (on his phone, laptop, tablet), that are used to fine-tune and personalize the model. Once the baseline model satisfies some requirement the model can be shipped to the client, which will eventually further train it, without sharing any user data with external actors.
7. AI and Advanced Biometric Services for Security
Artificial intelligence AI has become a fundamental aspect of our lives and demonstrates intelligence by machines. When employed with bio authentication
data it can provide a genuine authentication solution making it difficult to be fooled by cybercriminals. AI is enhancing biometric ID validation
for better security. Truly, in the future, it will get conceivable to identify whether an individual is traumatized or irate. 2020 would witness expansion in the use of this innovation with dependability and higher precision.