Deep Learning trends That could have a massive impact in 2019.

Oct 15, 2018 | 2685 Views

Deep learning is a part of machine learning which a part of Artificial intelligence is so Deep Learning is a part of Artificial Intelligence. So what does deep learning do? Deep learning is what gives machine the potential to act like humans. So if anything goes south you know who to blame. Deep learning, collects huge datasets and has immense potential because of Machine Learning that it can tackle difficult problems like language, image recognition and speech, it gives machines the potential to learn how a data combine into increasing high levels abstracted forms. With the help of neural networks deep learning has been able to give the desired result/ or the result it was looking for. Facebook has been using deep learning to recognize your friends in a given image automatically so you it can suggest you who else you can tag in it. 

Deep learning has a bright market
The market of Deep Learning is expected to cross $18 billion by the year 2024, with a CAGR growth of 42 percent. The potential of deep learning can be seen through its capability of doing algorithms that can turn complex data of image, video text and audio recordings and convert it in business friendly information. Modern online services are built on the strong shoulders of deep learning. Deep learning is being used by giants of the tech world like Amazon and Google in understanding of speech text of foreign language and Alexa virtual assistant, how a user thinks and works.
In the coming future years you can expect to have a deep learning dominated trends. Deep learning is going to have great impact on the technology and business world.
This post is about top 5 deep learning trends that will dominate the next year and even years coming after it.

1. Training Datasets Bias will Influence AI
Human bias is a significant challenge for a majority of decision-making models. The difference and variability of artificial intelligence algorithms are based on the inputs they are fed. Data scientists have come to a conclusion that even machine learning solutions have their own biases that may compromise on the integrity of their data and outputs. Artificial intelligence biases can go undetected for a number of reasons, prominently being training data biases. Bias in training datasets impacts real-world applications that have come up from the biases in machine learning datasets including poorly targeted web-based marketing campaigns, racially discriminatory facial recognition algorithms and gender recruiting biases on employment websites. 
2. AI will Rise Amongst Business and Society
Gone are the times when AI was the toast of sci-fi movies, but technology has finally caught up with imagination and adaptability. In the present times, AI has become a reality and amazingly, business and society encounter some form of artificial intelligence in their everyday operations.
Deep learning has dramatically improved the way we live and interact with technology. Amazon's deep learning offering Alexa is powered to carry out a number of functions via voice interactions, like playing music, making online purchases and answering factual questions. Amazon's latest offering, AmazonGo that works on AI allows shoppers to walk out of a shop with their shopping bags and automatically get charged with a purchase invoice sent directly to their phone. 
3. AI Reality, the Hype will Outrun Reality
Deep learning powered Robots that serve dinner, self-driving cars and drone-taxis could be fun and hugely profitable but exists in far off future than the hype suggests. The overhype surrounding AI and deep learning will propel venture capitalists to redirect their capital elsewhere to the next big thing like 4d printing or quantum computing. Entry bars for deep learning project investments will be higher and at that point, the AI bubble will plunge. To avoid that, technology needs to help users to recognize that AI, machine learning, and deep learning are much more than just buzzwords and have the power to make our every day much easier. Reality says the time is ripe to spend fewer efforts on the exploration of deep learning possibilities and instead focus on delivering solutions to actual, real-life problems.
4. Solving The 'Black Box' Problem with Audit Trails
AI and its adaptability come with one of the biggest barriers to its deployment particularly in regulated industries, is the explanation as to how AI reached a decision and gave its predictions. 2019 will mark a new era in creating AI audit trails explaining the nitty-gritties of how AI and deep learning reach a conclusion.
In the future times to come, AI will be explored and deployed for groundbreaking applications like drug discovery which can have a detrimental impact on human life if an incorrect decision is made. Thus, audit trails to AI and deep learning predictions are extremely important. 
5. AI Innovations will be Built on Cloud Adoption Capabilities
Come 2019 and beyond and business enterprises will seek to improve their technological infrastructure and cloud hosting processes for supporting their machine learning and AI efforts. As deep learning makes businesses innovate and improve with their machine learning and artificial intelligence offerings, more specialized tooling and infrastructure will be needed to be hosted on the cloud to support customised use cases, like solutions for merging multi-modal sensory inputs for human interaction (like think sound, touch, and vision) or solutions for merging satellite imagery with financial data for enhanced trading capabilities.


Source: HOB