Top Artificial Intelligence Trends You Should Consider For AI & Machine Learning Development

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Artificial Intelligence; possibilities of AI are innumerable and they easily surpass our most artistically fecund imaginations. We are really close to living in some sort of sci-fi, so it’s a good idea to have a look at the most possible and promising machine learning and AI trends for the upcoming 2019 and ask ourselves if we are ready for them. What all we read in science fiction novels or saw in movies like ‘The Matrix’ could someday materialize into reality.

In science fiction, artificial intelligence (AI) systems are often bent on overthrowing human civilization, or they are benevolent caretakers of our species. In reality, machine-learning is already with us, evolving out of search engines like Google and seeping into our everyday lives without much fanfare.

Artificial intelligence is 360 solution for business and government leaders review the right moves. But what’s happens in the lab, where discoveries by academic and corporate researchers will set AI’s course for the coming year and beyond? Team of researchers from Artificial Intelligence Lab homed in on the leading developments both technologists and business leaders should watch closely.

Courtesy: Google.com

Bill Gates, the founder of Microsoft, once said that ‘AI can be our friend’ and is good for the society.

Let’s have a look at the trends In AI in 2018 that will have a huge impact in years to come.

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Deep learning theory: The information bottleneck principle explains how a deep neural network learns.

What it is: Deep neural networks, which mimic the human brain, have demonstrated their ability to “learn” from image, audio, and text data. Yet even after being in use for more than a decade, there’s still a lot we don’t yet know about deep learning, including how neural networks learn or why they perform so well. That may be changing, thanks to a new theory that applies the principle of an information bottleneck to deep learning. In essence, it suggests that after an initial fitting phase, a deep neural network will “forget” and compress noisy data — that is, data sets containing a lot of additional meaningless information — while still preserving information about what the data represents.

Why it matters: Understanding precisely how deep learning works enables its greater development and use. For example, it can yield insights into optimal network design and architecture choices, while providing increased transparency for safety-critical or regulatory applications. Expect to see more results from the exploration of this theory applied to other types of deep neural networks and deep neural network design.

Capsule Networks: New type of deep neural network that learns with fewer errors and less data, by preserving key hierarchical relationships.

What it is: Capsule networks, a new type of deep neural network, process visual information in much the same way as the brain, which means they can maintain hierarchical relationships. This is in stark contrast to convolutional neural networks, one of the most widely used neural networks, which fail to take into account important spatial hierarchies between simple and complex objects, resulting in miss-classification and a high error rate.

Why it matters: For typical identification tasks, capsule networks promise better accuracy via reduction of errors- by as much as 50 percent. They also don’t need as much data for training models. Expect to see the widespread use of capsule networks across many problem domains and deep neural network architectures.

Deep Reinforcement Learning: This technique combines reinforcement learning with deep neural networks to learn by interacting with the environment.

What it is: A type of neural network that learns by interacting with the environment through observations, actions, and rewards. Deep reinforcement learning (DRL) has been used to learn gaming strategies, such as Atari and Go — including the famous AlphaGo program that beat a human champion.

Why it matters: (DRL)Deep reinforcement learning is the most general purpose of all learning techniques, so it can be used in most business applications. It requires less data than other techniques to train its models. Even more notable is that it can be trained via simulation, which eliminates the need for labeled data. Given these advantages, expect to see more business applications that combine DRL and agent-based simulation in the coming year.

Generative Adversarial Networks: A type of unsupervised deep learning system, implemented as two competing neural networks, enabling machine learning with less human intervention.

What it is: A generative adversarial network (GAN) is a type of unsupervised deep learning system that is implemented as two competing neural networks. One network, the generator, creates fake data that looks exactly like the real data set. The second network, the discriminator, ingests real and synthetic data. Over time, each network improves, enabling the pair to learn the entire distribution of the given data set.

Why it matters: GANs open up deep learning to a larger range of unsupervised tasks in which labeled data does not exist or is too expensive to obtain. They also reduce the load required for a deep neural network because the two networks share the burden. Expect to see more business applications, such as cyber detection, employ GANs.

Lean & Augmented Data Learning: Different techniques that enable a model to learn from less data or synthetic data.

What it is: The biggest challenge in machine learning (deep learning, in particular), is the availability of large volumes of labeled data to train the system. Two broad techniques can help address this: (1) synthesizing new data and (2) transferring a model trained for one task or domain to another. Techniques, such as transfer learning (transferring the insights learned from one task/domain to another) or one-shot learning (transfer learning taken to the extreme with learning occurring with just one or no relevant examples) — making them “lean data” learning techniques. Similarly, synthesizing new data through simulations or interpolations helps obtain more data, thereby augmenting existing data to improve learning.

Why it matters: Using these techniques, we can address a wider variety of problems, especially those with less historical data. Expect to see more variations of lean and augmented data, as well as different types of learning applied to a broad range of business problems.

Probabilistic Programming: A high-level language that makes it easy for developers to define probability models.

What it is: A high-level programming language that more easily enables a developer to design probability models and then automatically “solve” these models. Probabilistic programming languages make it possible to reuse model libraries, support interactive modeling and formal verification, and provide the abstraction layer necessary to foster generic, efficient inference in universal model classes.

Why it matters: Probabilistic programming languages have the ability to accommodate the uncertain and incomplete information that is so common in the business domain. We will see wider adoption of these languages and expect them to also be applied to deep learning.

Hybrid Learning Models: Approach that combines different types of deep neural networks with probabilistic approaches to model uncertainty.

What it is: Different types of deep neural networks, such as GANs or DRL, have shown great promise in terms of their performance and widespread application with different types of data. However, deep learning models do not model uncertainty, the way Bayesian, or probabilistic, approaches do. Hybrid learning models combine the two approaches to leverage the strengths of each. Some examples of hybrid models are Bayesian deep learning, Bayesian GANs, and Bayesian conditional GANs.

Why it matters: Hybrid learning models make it possible to expand the variety of business problems to include deep learning with uncertainty. This can help us achieve better performance and explainability of models, which in turn could encourage more widespread adoption. Expect to see more deep learning methods gain Bayesian equivalents while a combination of probabilistic programming languages start to incorporate deep learning.

Automated Machine Learning: Technique for automating the standard workflow of machine learning.

What it is: Developing machine learning models requires a time-consuming and expert-driven workflow, which includes data preparation, feature selection, model or technique selection, training, and tuning. AutoML aims to automate this workflow using a number of different statistical and deep learning techniques.

Why it matters: AutoML is part of what’s seen as a democratization of AI tools, enabling business users to develop machine learning models without a deep programming background. It will also speed up the time it takes data scientists to create models. Expect to see more commercial AutoML packages and integration of AutoML within larger machine learning platforms.

Digital Twin: A virtual model used to facilitate detailed analysis and monitoring of physical or psychological systems.

What it is: A digital twin is a virtual model used to facilitate detailed analysis and monitoring of physical or psychological systems. The concept of the digital twin originated in the industrial world where it has been used widely to analyze and monitor things like windmill farms or industrial systems. Now, using agent-based modeling (computational models for simulating the actions and interactions of autonomous agents) and system dynamics (a computer-aided approach to policy analysis and design), digital twins are being applied to nonphysical objects and processes, including predicting customer behavior.

Why it matters: Digital twins can help spur the development and broader adopting of the internet of things (IoT), providing a way to predictable diagnosis and maintain IoT systems. Going forward, expect to see greater use of digital twins in both physical systems and consumer choice modeling.

Explainable Artificial Intelligence: Machine learning techniques that produce more explainable models while maintaining high performance.

What it is: Today, there are scores of machine learning algorithms in use that sense, think, and act in a variety of different applications. Yet many of these algorithms are considered “black boxes,” offering little if any insight into how they reached their outcome. Explainable AI is a movement to develop machine learning techniques that produce more explainable models while maintaining prediction accuracy.

Why it matters: AI that is explainable, provable, and transparent will be critical to establishing trust in the technology and will encourage wider adoption of machine learning techniques. Enterprises will adopt explainable AI as a requirement or best practice before embarking on widespread deployment of AI, while governments may make explainable AI a regulatory requirement in the future.

Courtesy: Google.com

Conclusion

Overall, artificial intelligence is a fantastic opportunity for scientists and developers all over the world to make a sustainable ecosystem that’s more efficient for multiple purposes. The chance of machine making a mistake is much less than a human making a mistake; the state of development in multiple fields will shift to the whole new level. Like healthcare, eCommerce, logistics industries & autonomous factories are the few examples who are logically and globally accepting and implementing the trend of AI and IoT. If you are looking for any sort of consultants & development resources of machine learning & AI, you may get the assistance.