Even before the evolution of the computer age, scientists seem to have been captivated by the idea of making a machine that could behave as a human. In the last decade, we actually witnessed this idea turning into reality. All thanks to deep learning and artificial intelligence, human intelligence can be imitated and a wide array of tasks from thinking to learning, problem-solving, reasoning, and much more, can now be done.
As we move ahead with technology, we see an upsurge in putting AI to work. Many studies discovered that more than 90% of companies are either already using artificial intelligence in their operations today, or planning to do so in the near future.
Application of ML and Deep AI
•Marketing: Email and social media sentiment analysis uses textual indications to alert emotional states.
•Automotive: Driver less cars modelled on sensor information.
•Speech Recognition: Machine learning allows users to learn about speech recognition over time. The process is extensive training and an average precision of 95 percent.
•Computer Prediction: Used in various applications, for example, in the detection of car number plates and facial recognition features.
•Information Recovery: ML and DL are utilized for applications like search engines, both text-based and image-based searches.
•Medical Purposes: It also has a broad use in the medical sector such as, in the identification of cancer and detection of an anomaly by recognizing visual markers, independent of human programmers or diagnosticians.
•Others: NLP, used in applications such as an analysis of sentiments, picture tagging, online advertising.
Tips on Adopting Enterprise Machine Learning:
Machine learning has been evolving at a good pace, as organizations become aware of its potential and its impact on business operations and revenue. But truth be told, we are still stuck in the early stages of its adoption and are yet to deploy ML into operations, completely.
To remedy this situation and help you make the most of what enterprise ML has to offer, here is a list of tips and techniques that can help organizations in shifting from exploration to execution.
1.Implement specific roles for machine learning
New job roles in the ML field have already begun to work, such as “data operations specialists” and “machine learning engineers”, specializing in building and deploying machine learning models.
As a matter of fact, almost 40% of developed organizations have a designated ML engineer, doubling the number of organizations that are just beginning.
So, if your organization has an ML unit, make sure everyone knows it. Put into operation a designation that is specified for the purpose of machine learning implementation.
2.Introduce relevant machine learning success metrics
Team priorities are set by endowing data science to sophisticated organizations. Participants belonging to the most developed organizations are likely to use various performance metrics, including ML metrics, business metrics, and statistical metrics, and they measure for bias and fairness.
3.Setting up a model of machine learning differently
Development of machine learning models is distinct from the development of software.
As such, completing the model-building process of machine learning does not automatically translate into a fully working plan. Communities are still working to build tools that can help manage the entire lifecycle including model deployment, operations, and monitoring.
4.Develop powerful checklists for model building
Organizations with enormous experience in deploying machine learning systems have far more powerful model building checklists than their competitors, including transparency controls and data privacy checks already. About 53% of organizations belonging to businesses with comprehensive experience in ML check for privacy processes. In fact, GDPR mandates “privacy-by-design” or the incorporation of data protection from the initial design of a system.
Deep Learning at Work
IBM Watson
Naturally, without IBM Watson, no debate about modern AI is possible. IBM’s AI captured the attention of the mainstream and triggered a race to market machine learning and deep learning innovations. In addition to competing on TV shows, Watson has since helped financial firms fulfil their contractual commitments, and has enabled healthcare organizations to identify and treat diseases more quickly and accurately.
IBM has spent $240 million over 10 years on the new MIT-IBM Watson AI Lab. Aimed at advancing profound learning equipment, software, and algorithms, as well as finding methods to use AI to address healthcare, cyber security, and other challenges.
Microsoft Azure Cognitive Services
Although Microsoft has become a cloud powerhouse in the latest years, reflective of its Azure cloud computing platform, it is renowned for its Windows PC operating system and Office Suite of productivity apps.
The software giant Redmond, Wash. provides developers a range of Azure Cognitive Services dubbed deep-learning APIs (application programming interfaces). They typically involve image processing, speech recognition, natural language processing, smart search, and knowledge mapping that helps create problem-solving applications.
The software also provides an install able Microsoft Cognitive Toolkit that allows developers to train and experiment with deep learning algorithms on their local systems (64-bit Windows and Linux) before launching full-blown AI workloads in the cloud.
Google Cloud AI
Google has pioneered smart search that yields the outcomes that consumers are looking for. As another significant cloud provider, it has transformed the deep learning research that powers its products into a suite of services that the business calls Cloud AI.
Cloud AI enables developers to develop and train large-scale machine-learning models or infuse their applications with speech recognition, translations services, and more based on the company’s own neural net-based techniques.
Businesses may also use Cloud AI to obtain more value from their video content with the Cloud Video Intelligence API, which uses powerful deep-learning models based on frameworks such as Tensor-flow and applied to massive media platforms like YouTube.
Besides Enterprise Developers…
Apart from established IT companies, some AI start-ups are seeking enterprise software development services that can be used to develop smart business applications.
• Aidoc, based in Tel Aviv, has lately been awarded the CE mark — the “Conformité Européenne” label for goods that meet European standards for a deep-learning solution that automates workflows for radiologists in the field of head and neck imaging. The technology can spot abnormalities in medical scans, save time and improve patient care by assisting hospitals to prioritize cases that may need more instant attention.
• Babblabs – A US-based company which creates new technologies for speech processing, using sophisticated deep neural networks and audio signal processing techniques announced that it raised $4 million to further develop and market its speech comprehension and technology processing. The start-up uses deep neural networks to imagine faster, more accurate voice-enabled interfaces and cloud services that go beyond the patterned and error-prone interactions associated with current virtual assistants.
Looking into the Future
Universally, deep learning/artificial neural networks demand large quantities of tagged data for supervised learning, or huge quantities of unstructured data for unsupervised learning. Deep learning technology designers will either have to invest considerable time in labeling and inputting data into the neural network, or will have to input millions of unstructured objects in order to gain unsupervised learning.We live in a data-intensive world where an ample amount of data is not a big deal but rather tagging enough data, or introducing sufficient unlabeled data to a neural network is a concern.
Even though processing power has expanded and prices have fallen, intensive computations still require substantial investment in systems and support. Deep learning, nevertheless, has lucrative instances of use across many distinct verticals of enterprises. In order to build these practical applications, powerful corporate businesses like Google and Facebook invest in deep learning, and other developers take up the slack.
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