Deep Learning Market

Drug Discovery and Medical Treatment

Deep learning plays a significant role in drug discovery and medical treatment as it is increasingly being used for automating the process of invention of new chemical entities and mining of large databases in health-privacy-protected vaults. The failure of allopathy to produce consistent results across varied sets of patients along with the genomics revolution has spurred the use of precision medicine. Precision medicines have enabled scientists and doctors to take certain decisions of treatment based on whether a drug will be effective, ineffective, or toxic, by identifying the genomics of patients and their diseases. Deep learning has widespread applications in the development of precision medicine. One of the major challenges in taking advantage of the advances in genomics is to decode the multi-faceted regulatory system that controls gene expressions. The regulation of gene expression involves many factors involving DNA methylation, regulatory RNAs, and transcription factors. Deep learning neural networks such as CNN and prediction network offer an ideal solution to meet these challenges in the field of drug discovery. So far, various deep learning models have been developed to precisely predict new molecular entities as plausible therapeutic scaffolds. Pharmaceutical giants such as Bayer Healthcare and Roche have contributed to computer-assisted drug design technologies for developing optimized pharmacophores. Increasing developments in deep learning models are expected to transform the fields of drug discovery and medical treatment.

Predictive Maintenance

Predictive maintenance is used for reducing the downtime and cost of maintenance under the premise of zero failure manufacturing by utilizing real-time data to forecast potential faults. Predictive maintenance depends heavily on the foundation of correlation techniques. Deep learning has widespread applications in predictive maintenance for mechanical and electrical systems in industries of manufacturing. The introduction of industry 4.0 has changed the manufacturing processes, maintenance management, and maintenance strategies significantly. Industry 4.0 has combined strengths of optimized manufacturing with internet technologies, cyber-physical systems (CPS), internet of things (IoT), and internet of services (iOS) which has led to the generation of large amounts of data, which can be fed to deep learning to develop new maintenance strategies. Deep learning has offered computation of hierarchical features or representation from objective data. Deep learning offers feature extraction without the need of human expertise through a generalized self-learning procedure which has made it ideal for use in predictive maintenance in the manufacturing industry. Currently, deep learning algorithms such as deep neural networks and deep belief networks have been successfully applied in predictive maintenance. Classification algorithms such as SVM (Support Vector Machine), SOM (Self-organizing map), and BPNN (Back-Propagation Neural Networks) are being used for health diagnosis of complex systems such as aircraft systems and electric power transformers. Deep learning algorithms such as DNN (deep neural network), SAE (Sparse Autoencoder), DBN (Deep Belief Network), LSTM (Long Short-Term Memory), and CNN (Convolution neural network) offer superiority in certain processes such as degradation mapping, failure identification, fault characteristics mining, feature extraction about failures from raw input data, and knowledge discovery capabilities over conventional machine learning algorithms.

Customer Service Management and Personalized Service Offerings

Machine learning has been used for marketing automation for over a decade. Machine learning algorithms enable business owners to personalize customer experience based on their history of interactions, like purchasing habits, behavioral traits, and digital preferences. Deep learning technology, on the other hand, has further improved personalization processes by including customer intent to interaction history of the customers. The recommender systems used by businesses ranging from eCommerce stores to publishers and marketing agencies to drive sales, increase engagement ,and improve overall user experience are increasingly using deep learning algorithms due to the benefits they offer over traditional machine learning algorithms. Personalized customer services such as chatbots rely on deep learning models to offer personalized responses to customers. Deep learning algorithms are also being used to drive customer retention. Deep learning caters to several factors, such as customer preference, personal preferences, spending patterns, and micro preferences and combines it with external factors, such as weather, to generate highly customized relevant suggestions which significantly increases customer retention. Moreover, deep learning algorithms such as CNN (Convolution neural network), RNN (recurrent neural network), and LSTM (Long Short-Term Memory) are being used for sentiment analysis on various social media websites which has further benefitted the development of marketing automation. When it comes to marketing automation in terms of personalization customized responses deep learning is considered as the future.

Logistics Optimization

Deep learning is currently transforming the logistics industry. Currently, the most prominent application area is predictive analytics. In the logistics industry, deep learning models enable shippers to optimize carrier selection, rating, routing, and quality control processes that save costs and improve efficiencies. Deep learning models optimize analytics solutions to consider dynamic attributes like weather or traffic and self-evolve over time to recognize patterns which optimizes the logistics operations significantly. The ability of deep learning models to utilize data across multiple systems and data sources like GPS systems, historical pricing performance, and FMCSA (Federal Motor Carrier Safety Administration) enables shippers to predict demand, analyze trends in supply chains, monitor seasonal calendars, and track daily patterns within lanes. Moreover, natural language processing, which is a part of machine learning, has optimized supply chains significantly by speeding up data entry and auto-populating form fields. Natural language processing is used for customizing customer responses by auto-populating shipping orders, bills of lading, and other transactions. Moreover, deep learning has applications in other areas of the supply chain such as predictive equipment maintenance, yield optimization, procurement analytics, and inventory optimization.

Voice and Image Recognition

Voice and image recognition are some of the major applications of deep learning. Deep learning is extensively used for image recognition, object detection, feature mapping for computer vision. Deep learning models for object detection have already surpassed human performance. CNN, a deep learning algorithm, is increasingly being used for purposes of object detection and feature extraction due to various advantages such as ruggedness to shifts and distortion in the image, fewer memory requirements, and easier and better training. Object detection is used across different fields such as robotics, automation in manufacturing, medical image detection, safety systems, and more. Voice recognition is another application with the widespread use of deep learning models. Automatic speech recognition is still a challenging task due to different factors such as different accents, dialects, or pronunciations, different styles, and different rates of speakers. Moreover, the presence of environmental noise, reverberation, different microphones, and recording devices results in additional variability which makes it a difficult task for conventional algorithms. Deep learning due to its unsupervised learning type is becoming a mainstream technology for speech recognition. Deep learning algorithms such as deep belief networks are being used for speech recognition and feature coding at an increasingly larger scale. With further development in the technology, deep learning is expected to transform image and voice recognition fields

Cloud computing adoption has been increasing at an unprecedented rate. Organizations are increasingly using software as a service (SaaS), platform as a service (PaaS), and infrastructure as a service (IaaS) solutions due to the benefits they offer in terms of return on investments. Cloud-based services offer increased scalability and security, which has made it more attractive to businesses of all sizes. This growth in the adoption of cloud-based services has positively impacted the deep learning market. Companies such as Amazon, Google, and Microsoft have invested heavily in the development of deep learning and AI. Cloud platforms such as Amazon Web Services, Microsoft Azure, and Google Cloud Platform have started offering machine learning and deep learning-based services which has benefited the progress in the development of deep learning models. The cloud-based machine learning services offer a pay-per-use model for large AI or machine learning workloads. These services significantly benefited companies building sophisticated deep learning models which require large compute clusters. Cloud-based machine learning services have offered low-cost options for moving deep learning models to the cloud and have eliminated the problem of the need of deep knowledge of AI, deep learning theory, or a team of data scientists for the development of deep learning models. These benefits and the increasing adoption of cloud-based deep learning services have driven the growth of the global deep learning market.

Agri technology and precision farming have given rise to new scientific fields that use data-intensive approaches to drive agricultural productivity while minimizing its environmental impact. Deep learning has emerged as an ideal solution to deal with analytics of different types of data involved in digital and precision farming. Deep learning algorithms such as ANN, DNN and DBN offer significant opportunities for different application areas in precision farming such as yield prediction, disease detection, weed detection, crop quality analytics, species recognition, livestock management, water management, and soil management.

In the field of medicine, deep learning has potential growth for drug discovery and computational biology. Deep learning models are expected to transform the field of drug discovery due to the advantages that they offer in terms of predicting new molecular entities to match with the genomes of patients.

Marketing automation is another field which is expected to be transformed by deep learning models in the coming years. Deep learning is used to provide a convenient, informed, and intelligent customer experience. It is a tool that can be used to gain a competitive advantage and drive enterprise growth. Deep learning technology has transformed marketing to ensure enhanced customer experience by offering speedy, automated, and hassle-free services. These products and services include virtual assistants and chatbots. Presently, customers prefer voice-activated virtual assistants such as Apple’s Siri and Amazon Alexa to search for products online and control home appliances, among other functions. Enterprises also use deep learning for customer analytics as it offers them the opportunity to improve customer experience and pave the way for new revenue streams. The advantages that deep learning models offer in the fields of agriculture, healthcare, and marketing automation present a noteworthy growth opportunity for the global deep learning market.

The demand for deep learning is growing significantly due to its advantages, such as improved efficiency and cost reduction. However, the designing and creation for deep learning models require a high level of skilled expertise as they are highly complex. The solutions that deep learning offers to businesses are very critical for gaining a competitive advantage in their respective markets; this has further increased the need for skilled labor. There is a remarkable lack of technical experts for both software and hardware development in the field. According to Tencent, there are just around 300,000 AI practitioners and researchers globally, while the demand for deep learning skilled human resources is in the millions. The lack of skilled human resources in the field of deep learning has driven companies to gain skills by acquiring innovative startups. The recent examples of such acquisitions include Microsoft acquiring Maluuba, TomTom acquiring autonomous, and Uber acquiring Geometic. Companies such as Google, have teamed up with MOOC pioneer Coursera to launch online courses to increase the awareness and skills of professionals in deep learning. However, the current lack of skilled professional in the research and development of deep learning technology is expected to hamper the growth of the global deep learning market in the short term.

Deep learning algorithms unlike machine learning algorithms do not require labelled datasets for the purpose of training. However, due to its ability to train without the requirement of labelled datasets the amount of data needed for training is much higher compared to machine learning algorithms. The task of deep learning algorithms is two folded as it first need to understand and recognize the problem of the domain before attempting to solve the problem. For learning the domain of the problem, deep learning algorithms requires large number of parameters which require large amounts of data. Tasks such as speech recognition and image recognition require high level of abstraction and hence require large number of parameters and data for training the deep learning algorithms. Researchers developing deep learning algorithms have to feed terabytes of data to the deep learning algorithms for them to perform basic tasks such as learning a language. This process is highly time consuming and requires tremendous data processing capabilities. Furthermore, availability of huge datasets required for training is low for certain areas such as industrial applications. Requirement of massive datasets for training deep learning algorithms and high processing capabilities to perform the training process are together considered to be a challenge for the deep learning market. However, efforts are being made to bypass and reduce the high requirement of data sets for training machine learning algorithms through approaches such as use of small datasets for automatically creating new and similar data.