The future we can face with AI
Improved voice, speech, image, and video recognition will change the way we interact with our devices. In the coming years, we will continue to see great improvements in the quality and fidelity of speech, speech, image, and video recognition, and our ability to classify results will improve significantly. Cheap and ubiquitous sensors and cameras will provide ever-increasing streams of data for real-time processing. This real-time requirement, coupled with cheaply available processing power and storage, will make it much more cost-effective and efficient to process the data at the point of collection, and eventually learn and act on the data locally. 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 better able to understand the context of our requests, which in turn will enable them to negotiate an ever-widening range of capabilities. If conversation-driven AI assistants end up completely displacing more traditional GUI interfaces into our daily activities.
Beyond current command-and-control-style personal assistant systems, improvements in conversational systems will be the catalyst for robots to finally come into general use as household items. Whenever you fly, much of the journey is done by a machine, not the pilot. Autonomous cars and autonomous drones seem inevitable.
AI is being, and will continue to be, quietly embraced by businesses, allowing them to extract insights from all data being generated, not just structured data.
AI will continue to advance to take on decision-making tasks. Automated fleet management, inventory management, and candidate resume screening are just a few examples.
Each step forward in core AI research is opening up our abilities to solve new classes and scales of problems, which in turn allows for accelerated research in almost all scientific domains, for the betterment of humanity.
They can then teach themselves, either supervised or even unsupervised, leading to successful implementations in a variety of specialized application areas. AI will grow beyond its role as content curator and analyzer and become much more important in generating and nurturing content in the first place. These types of systems could be used in education: imagine a teacher who is learning alongside the student.
Fast forward and we will begin to see hyper-personalized hypothesis-generating systems, operating on our background data, such as our genomics, along with measurements from our wearable devices and other biological monitors, to provide each of us and our clinicians with a lens high-precision: and a crystal ball, offering valuable insights into environmental and behavioral impacts on our health.
AI will also be used to interpret human brain activity in a way that can decipher intent, enabling augmentation to overcome physical challenges and new methods of communication for and with disabled patients.
With AI moving to control more devices and content sources, collaboration between these semi-autonomous AI agents will yield great benefits.
AI will also impact designers and programmers, automating much of the processes involved, mapping their wishes, explicitly communicated or even implicit, to achieve creations that meet those requirements. In parallel, this will result in increased satisfaction for people who interact with these AI-automated designs/programs, creating surprise and delight by continually transforming the design or program as the system takes into account learnings from interactions with other users. .
AI has the potential to greatly improve things like healthcare, education, poverty, and security. AI machines can already do some very beneficial things today that humans simply will never be able to do. If we harness that to augment what humans do well, AI could have a positive impact on society, business, and culture on the order of magnitude of the Internet itself. This will allow AI to be used to scale the human mind, not replace it.
Many of the answers lie in the vast amount of medical data already collected. Ayasdi uses artificial intelligence algorithms such as deep learning to allow doctors and hospitals to better analyze their data. Through her work, doctors have been able to identify previously unknown diabetes subtypes that could lead to a better understanding of therapies that might work best for certain types of patients. Enlitic and IBM are using similar AI algorithms, but to more accurately and efficiently detect tumors in radiology scans, potentially even speeding up the search for a cure for cancer.
AI-based solutions already on 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 the predictive capabilities of AI for advanced threat detection on a global scale. SiftScience, Cylance, and Deep Instinct are using it for fraud prevention and for the security of endpoints such as smartphones and laptops. These technologies will dramatically expand the reach and scale of security professionals, enabling them to detect threats long before they strike.