Source – menafn.com
Technavio’s latest market research report on the deep learning market in the US provides an analysis of the most important trends expected to impact the market outlook from 2017-2021. Technavio defines an emerging trend as a factor that has the potential to significantly impact the market and contribute to its growth or decline.
According to Bharath Kanniappan, a lead analyst at Technavio for robotics research, “The deep learning market in the US is expected to grow at a phenomenal CAGR of over 57% during the forecast period. Industries across the US are striving hard to channelize and optimize multiple facets of operations, including data analysis, storage, strategy, and decision-making. Deep learning has surfaced as a powerful tool that assists industries in improving the programming of automated machines/equipment and inducing self-learning capabilities.”
The top three emerging market trends driving the global deep learning market in the US market according to Technavio research analysts are:
- Advances in deep learning
- Reinforcement learning
- Combating security threats using AI
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Advances in deep learning
With industries harnessing deep learning technology to optimize operations and make real-time decisions, modular capabilities in deep learning will aid visual design, configuration, and training new models obtained from pre-existent building blocks. A major structural change will emerge as a result, known as transfer learning, which will enable experiential solving of similar cases.
As deep learning technology gets adopted by masses, the market will progress toward a self-service cloud-enabled delivery model. This cloud-based platform will deliver fast results and would be useful in overcoming technical difficulties encountered in deep learning algorithm. This evolution in deep learning market will pave the way for a new wave of industrial revolution. Industries will switch from their traditional mode of disconnected systems and reactive approach to an integrated and proactive approach based decision-making.
Reinforcement learning is a specialized form of supervised learning with a provision of training information provided by the environment. The learner/user in reinforcement learning needs to uncover actions that generate the best results, by being a part of the decision-making process. Instead of following instructions, the learner can override the system-generated commands to take decision on its own. Reinforcement learning is an evolved version of machine learning and superior in terms of results delivered. Unlike supervised learning, reinforcement learning exhibits adequacy in situations when there is an absence of a knowledgeable supervisor. In such unfamiliar situations, an agent is required to be able to learn from the interface and by using its own experience. This is where reinforcement learning is expected to showcase its advantages.
Combating security threats using AI
Leakage of sensitive information and security threats are some of the major problems faced by end-users while deploying automation solutions. In recent times, several instances of cyber security concerns were reported in manufacturing industries such as oil and gas, automotive, pulp and paper, chemical and petrochemical, food and beverages, and pharmaceutical.
Traditional systems that ensure cyber security ar reliant on signature-based detection, network perimeter security model, and firewalls. However, this approach is not a highly robust method as continuous exchange of confidential information taking place through emails and websites is potentially vulnerable to malware.
“AI technologies can help end-users address several issues related to cyber-attacks including firewall failure, security threat to voluminous sensitive data, and scalability challenges. Advanced security products that are based on AI-technologies like deep learning can recognize and destroy malware rapidly during its development. In this way, businesses can ensure security and integrity of their critical data,” says Bharath.
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