Artificial intelligence (AI) is the fastest-growing field of computer science and technology, achieving great success in a very short period. It is a brand new era of computer science, dealing with the development of machines that can make decisions on their own.
The use of AI is increasing rapidly in our day-to-day life, i.e., from reading our emails to getting directions for driving vehicles, as it is very fast, reduces our efforts, and makes our life easy.
As its name indicates, AI means man-made intelligence with the ability to understand, learn and think. This post will look at some of the top 17 AI techniques and models increasingly used in 2022 as alternatives to more classical techniques to model environmental systems.
1. Automated Machine Learning (AutoML)
Some businesses want to abandon traditional machine learning methods, which are time-consuming and difficult to implement and require data scientists, AI experts, and engineers. AutoML works uniquely, combining raw data and models to reveal the most pertinent information. Google, Amazon, and Microsoft all have AutoML products in their cloud services.
2. Continuous Learning
Deep learning techniques are currently assisting systems in learning to solve complex tasks like what humans can do—but these tasks are still specific, such as beating a human at a game. They also require a set of steps: collect data, determine the goal, and implement an algorithm. This procedure necessitates the use of humans and can be time-consuming, particularly in the early stages when supervised training is required. Continuous learning is more about self-directed and incremental skill development, and researchers will continue to push the boundaries of this field.
3. Federated Learning
Federated learning is a method for spreading machine learning to the edge. It’s a framework created by Google researchers in 2016 that allows algorithms to use data from devices like phones and smartwatches without jeopardizing user privacy. Hence, the amount of research in this area has recently exploded.
4. General Reinforcement Learning Algorithms
Single algorithms that can learn multiple tasks are being developed by researchers. DeepMind, the company behind AlphaGo, which taught itself to play Go at the level of a human grandmaster, is continuing its research. According to DeepMind, MuZero mastered multiple games without being told the rules, which is a “significant step forward in pursuing general-purpose algorithms.” DeepMind researchers proposed that artificial general intelligence could be achieved solely through reinforcement learning in their seminal paper “Reward Is Enough,” published in 2021.
5. Graph Neural Networks
Because we perceive scents through our brains’ millions of sensory neurons and because scents are multifaceted, predicting how something will smell extremely difficult. How would you describe the smell of an orange, for example? Sweet? Bright? Grassy? Every descriptor is distinct. Smell classification is difficult because it necessitates a multi-label system. GNNs (neural graph networks) are deep neural network that works with graphs as inputs. GNNs are used to detect and predict odors at the molecular level and for various chemical and biological processes. Researchers at the Broad Institute, for example, used them to find antibiotic compounds with no toxic side effects.
6. Hybrid Human-Computer Vision
AI is still not capable of fully functioning without the help of humans. To achieve greater accuracy, hybrid intelligence systems combine humans and AI systems. The Army Research Laboratory in the United States has developed a system that combines a brain-computer interface with computer vision technology. It enables a person to quickly see and sort images in her field of vision. CloudSight is an image captioning company that uses a hybrid crowdsourced computer vision system. Pandora is a set of hybrid human-machine methods and tools for understanding system failures proposed by Microsoft researchers. It uses human and machine observations to explain input content and system architecture problems.
7. Machine Image Completion
A computer system can patch and fill holes in pictures if it has access to enough images—say, millions and millions. Professional photographers and anyone who wants to take a better selfie will benefit from this capability. If a mountain’s foreground is out of focus, or your skin has an unsightly blemish, another version will soon be able to be swapped in to create the perfect image. As a result of the widespread adoption of technology, there will be significant biases and other pitfalls to avoid.
Image generation algorithms, for example, frequently reflect deeply culturally embedded racism and sexism. When you typed “CEO” into Google Images a few years ago, the first image that came up was of CEO Barbie. Carnegie Mellon University researchers trained a system to autocomplete images of men and women cropped below the neck in an experiment. The system autocompleted him wearing a suit in pictures of men. The system identified 53 percent of women wearing a low-cut top or bikini, including U.S. Rep. Alexandria Ocasio-Cortez (D-NY).
8. Model-free Approaches to RL
Dreamer is a reinforcement learning (RL) agent that employs backpropagation through model predictions to learn long-sighted predictions using a world model. Using a graphics processing unit (GPU), you can create models from raw images and learn from thousands of predicted sequences in real-time (GPU). This new method uses an imagined world to solve long-horizon problems.
9. Predictive Models Using Incomplete Data
Computer vision systems are becoming more intelligent. From a single color image, neural networks can predict geometry. DeepMind created a generative adversarial network (GAN) that creates videos from images in 2019. For instance: Consider a photograph of a basketball player. The GAN deduces what happened next based on his posture, face, and other data in the image and creates a video clip of the action.
Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) used YouTube videos and TV shows like “The Office” and “Desperate Housewives” to train computers to predict what humans would do next. The system developed by CSAIL predicts whether two people will hug, kiss, shake hands, or slap a high five. SinGAN is an unconditional generative scheme that can manipulate and enhance images—draw a mountain, and it will generate a synthetic photograph that looks realistic. This research will one day allow robots to navigate human environments more easily—and interact with humans by reading our body language. Retail, manufacturing, and educational environments may all be relevant.
10. Neuro-Symbolic AI
Since the 1950s, AI has progressed along two conceptual tracks: symbolic (machines that use a base of knowledge and rules to represent concepts) and non-symbolic (machines that do not use a base of knowledge and rules to represent concepts) (machines that use raw data to create their patterns and representations of concepts). Classic AI is the former because it more closely resembles our understanding of human thought—and the original goal was to teach machines to think like us. Researchers are developing new neural networks that combine learning and logic rather than relying on human programmers to sort, tag, and catalog data. Symbolic algorithms will help speed up the process, resulting in more reliable systems that don’t always require human training.
11. Real-time Machine Learning (RTML)
A major challenge in AI is building machines that can proactively collect and interpret data, spot patterns, incorporate context, and ultimately learn in real-time. New RTML research shows that models can be adjusted in real-time using a continuous data flow. This marks a significant shift in the way data is moved and retrieved. Although all major tech companies are working together to advance RTML, the National Science Foundation has launched a $10 million grant program to catalyze research in this area.
12. Vokenization
GPT-3 models are taught syntax and grammar rather than creativity or common sense. As a result, University of North Carolina–Chapel Hill researchers have combined language models with computer vision. Humans learn in multiple layers and dimensions. As a result, vokenization, a new technique for extrapolating language-only data, contextually maps language “tokens,” or the words used to train language models, related images, or “vokens.” Auto-generated image captions, for example, frequently fail to infer context. Vokenization would allow machines to see what’s inside objects rather than just recognize them.
13. DALL-E
DALL-E is an OpenAI model trained to manipulate visual concepts using language. It starts with a natural-language prompt and then generates a series of images illustrating its interpretation of the intended meaning. The model, named after Spanish surrealist artist Salvador Dali and the Pixar robot WALL-E, is based on zero-shot capabilities, which means it can perform generative tasks without examples. Researchers gave DALL-E the prompt “an avocado-shaped armchair” as an early example. It came back with various options, including a line drawing and an image of a chair that looked like it came from a furniture catalog.
14. GPT-3
GPT-3 is a massive artificial intelligence that generates human-like language. The system, developed by OpenAI, can now generate text indistinguishable from human writing. We wrote one of the scenarios in this volume using GPT3 and only lightly edited it for length. Professors graded the anonymous papers in which GPT-3 competed against college students in an essay writing contest. It received mostly Bs, just like human students.
However, the AI has a strong anti-Muslim bias. The neural network was tested on tasks like prompt completion, analogical reasoning, and story generation by Stanford and McMaster University researchers. They discovered that a Muslim-violence bias appears consistently and creatively in many model use cases. Another example of bias creeping into our automated systems. If AI develops, it will cause problems throughout society if left unchecked.
15. Google’s LaMDA and MUM
Sundar Pichai, Google’s CEO, demonstrated a model capable of managing open-ended conversations at last year’s I/O conference. After an initial prompt, LaMDA appeared to be closer to a natural conversation, following an infinite number of paths. Using natural language (text) and various images, Google’s Multitask Unified Model, or MUM, can understand complex queries during a search. MUM could be the future of search, allowing users to converse naturally while finding what they’re looking for.
16. Switch Transformer
Last year, Google Brain published a significant paper demonstrating a new language model architecture. This artificial intelligence model for natural language processing scales up to 1.6T parameters while reducing training time. Many language models are complicated and dense; the switching transformer’s breakthrough is that it lowers computational costs while increasing accuracy and power.
17. Chinese Models: Wu Dao 2.0 and M6
Wu Dao 2.0 and M6, two models, developed specifically for the Chinese language market, were released last year. Wu Dao’s research team compares it to GPT-3 but claims the network is bigger. Meanwhile, Alibaba’s M6 is a cost-effective model that lowers computing costs while lowering the carbon footprint typically associated with training models on large datasets.
