Deep Learning For Real-Time Human Activity Recognition On Mobile Phones The Future We Can Face With AI

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The Future We Can Face With AI

Better speech, audio, image and video recognition will change the way we interact with our devices. In the next few years, we will continue to see great improvements in the quality and fidelity of speech, audio, image, and video recognition, and our ability to classify results will improve significantly. Inexpensive and affordable sensors and cameras will provide for ever-increasing data processing in real-time. This real-time requirement, coupled with affordable and available processing power and storage, will make it much more cost-effective and efficient to store data locally and, ultimately, learn and act on local data. We will see these systems widely adopted in industrial automation systems, factory operations, security systems, agriculture, traffic and transportation, and many other fields.

Personal assistants will become more acceptable to us as they become more personalized to our needs and more able to understand the scope of our requests, which in turn will allow them to pursue ever-widening capabilities. Are conversational-driven AI assistants completely replacing traditional GUI interfaces in our daily activities.

In addition to current command-and-control style personal assistant systems, improvements in communication systems will be the catalyst for eventually using robots as household items. Whenever you fly, most of the journey is done by the machine, not the pilot. Self-driving cars and autonomous drones seem inevitable.

AI has been, and will continue to be, quietly adopted by companies, allowing them to extract knowledge from all the data they generate – and not just structured data.

AI will move towards taking over decision-making tasks. Automated fleet management, inventory management, and re-candidate review are just a few examples.

Advances in basic AI research always open us to solve new classes and dimensions of problems, which in turn accelerates research in almost every field of science, for the betterment of humanity.

Then, they can learn on their own, whether on a supervised or unsupervised basis – leading to successful deployment in many specialized application areas. AI will outgrow its role as a content curator and analyzer and become much more important in the production and enhancement of content in the first place. These types of systems can be used in education: think of a teacher learning alongside a student.

Fast forward and we’ll start to see hyper-personalized hypothesis-generating systems that will work on background data like our genomics, with measurements from our clothes and other biological monitors, so that each of us and provide our doctors with a very accurate lens – and a crystal ball – to offer valuable insights into the effects of environment and behavior on our health.

AI will also be used to use human brain activity in a way that can decipher intent, overcome increasing physical challenges and new methods of communication for and with disabled patients.

With AI controlling more devices and content sources, collaboration among these semi-autonomous AI agents will yield a huge benefit.

AI will also influence designers and programmers, automating many related processes, mapping their needs, clearly communicating or implying, to achieve creations that meet those needs. In parallel, this will lead to the satisfaction of people who interact with these automated AI designs/programs, constantly changing the design or program to create surprise and delight as the system learns from interactions with other users.

AI has the potential to vastly improve things like healthcare, education, poverty and security. AI machines today can already do some very useful things that humans simply cannot do. If we use it to enhance what humans do well, AI can have a positive impact on society, business and culture on the order of magnitude of the internet itself. This would allow AI to be used to measure the human mind, rather than replace it.

Many of the answers lie in the large amount of medical data that has already been collected. Ayasdi uses AI algorithms like deep learning to enable doctors and hospitals to better analyze their data. Through their work, medical doctors have been able to identify previously unknown types of diabetes that can lead to a better understanding of medications that may work better for certain types of patients. Enlitic and IBM are using similar AI algorithms, but to find tumors in radiology cells more accurately and efficiently, and even potentially speed up the development of a cure for cancer.

AI-based solutions already in the market can be more proactive and can prevent attacks in the pre-execution state by identifying patterns and anomalies associated with malicious content. Secureworks uses AI predictive capabilities for advanced threat detection on a global scale. SiftScience, Cylance, and Deep Instinct use it for fraud prevention and endpoint security, such as smartphones and laptops. These technologies will dramatically expand the scope and scope of security professionals and allow them to hopefully detect threats well before they actually strike.

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