The mission to unleash human creativity : Sony announced the establishment of SONY AI
By Megha Malohotra:-
Tokyo, Japan – Sony Corporation, also known as “SONY” recently announced the establishment of Sony AI. This new organization, with offices globally in Japan, Europe, and the United States, will advance fundamental research and development of AI (artificial intelligence). Sony AI will drive the research and development of AI in both physical and virtual space through multiple world-class flagship projects as well as other explorative research projects, including AI ethics.
Artificial Intelligence (AI) is the simulation of human intellect by machines, particularly computer frameworks. Explicit uses of AI incorporate skilled frameworks, Natural Language Processing (NLP), speech recognition and machine vision.
Artificial intelligence programming emphasize on the three intellectual and comprehensive skills : 1. Learning that focuses on procuring data and setting rules to convert data into functional information. These rules, known a “algorithms” cater steps and guides how to complete a task; 2. Reasoning wherein emphasis is on selecting the correct algorithm to reach the desired outcome; and 3. Self-Correction which is outlined to constantly fine-tune the algorithms ensuring as precise and apt results as possible.
Be it SIRI or Alexa or self-driving cars, artificial intelligence (AI) is progressing expeditiously. The science fictions often portray AI as robots with humanly characteristics but AI can incorporate anything from Google’s search algorithms to IBM’s Watson to autonomous weapons.
Types of Artificial Intelligence
AI can be categorized into four types :
- Reactive Machine: These AI systems are task specific and have no memory for instance, Deep Blue, an IBM chess program.
- Limited memory : These AI systems have memory and can use past experiences to inform future decisions. Some of the decision-making functions in self-driving cars are designed this way.
- Theory of mind : This type of AI enables to infer human intentions and predict behavior, a necessary skill for AI systems to become integral members of human teams.
- Self-awareness : AI systems have a sense of self, which gives them consciousness. Machines with self-awareness understand their own current state. This type of AI does not yet exist.
Strong AI vs. weak AI
AI can be classified as either weak or strong.
Weak AI, also known as narrow AI, is an AI system designed and trained to complete a particular task. Industrial robots and virtual personal assistants, such as Apple’s Siri, use weak AI.
Strong AI, also known as Artificial General Intelligence (AGI), describes programming which replicates the cognitive abilities of the human brain. When presented with an unfamiliar task, a strong AI system can use fuzzy logic to apply knowledge from one domain to another and find a solution autonomously.
Augmented intelligence vs. artificial intelligence
Some industry geniuses accept AI is firmly connected to mainstream society, and this has made the overall public have unlikely assumptions regarding how AI will change the work environment and life ordinarily. Some researchers and advertisers trust the label “Augmented Intelligence, which has a progressively neutral meaning, will assist people with understanding that most usage of AI will be weak and essentially improve services and commodities.
The idea of the technological singularity – a future managed by an artificial super intelligence that exceed the human brain’s capabilities to get it or how it is moulding our existence staying within the domain of sci-fi.
Examples of AI technology
AI has incorporated different types of technology like :
- Automation : Automation tools can expand the volume and types of tasks performed when paired with AI technologies for instance the Robotic Process Automation (RPA), a type of software which automates repetitive, rules-based data processing tasks primarily done by humans. When combined with machine learning and emerging AI tools, RPA can automate bigger sections of enterprise jobs, enabling RPA’s strategic bots to pass along intelligence from AI and react to process changes.
- Machine learning : This is the science of getting a computer to act without programming. Profound learning is a subset of machine learning which straight forwardly can be thought of as the automation of predictive analytics. There are three types of machine learning algorithms:
- Supervised learning wherein data sets are labelled for detecting patterns and label new data sets.
- Unsupervised learning in which data sets are sorted instead of labelling according to similarities or differences.
- Reinforcement learning wherein data sets after performing an action or several actions, the AI system is given feedback.
- Machine vision : This innovation gives a machine the ability to see. Machine vision apprehends and analyses visual data using a camera, analog-to-digital conversion and digital signal processing. Often compared to human eyesight, machine vision isn’t limited by biology and can be programmed to see through walls, for example. It is used in a number of applications from signature identification to medical image analysis.
- Natural language processing : It refers to the processing of human language by a computer program. One of the older and best-known examples of NLP is spam detection, which looks at the subject line and text of an email and mark it as spam if it’s junk. The Current approaches to NLP are replied upon machine learning. NLP tasks include text translation, sentiment analysis and speech recognition.
- Robotics : This field of engineering emphasis the design and manufacturing of robots. Robots are often used to execute tasks that humans are incompatible to perform. For instance, robots are used in assembly lines for car production or by NASA to transmit large objects in space. Researchers are also using machine learning to make robots that can interact in social settings.
- Self-driving cars : Autonomous vehicles use a blend of computer vision, image recognition and profound learning to build automated skill at piloting a vehicle while staying in a given lane and avoiding unexpected obstructions, such as pedestrians.
Applications of AI
Artificial intelligence is rapidly on its way to a variety of markets like : .
- AI in healthcare : The biggest bets are on improving patient outcomes and minimising costs. The organisations that apply machine learning to make advanced and faster diagnoses than humans. One of the best-known healthcare technologies is IBM Watson. It comprehends natural language and can react to questions made. The framework mines patient data and other accessible data sources to form a hypothesis, and then presents with a confidence scoring pattern. Other AI applications include using online virtual health assistants and chatbots to help patients and healthcare customers find medical data, schedule appointments, comprehend the billing process and complete other managerial processes. An array of AI innovations are also being used to forsee, fight and understand pandemics such as COVID-19.
- AI in business : Machine learning algorithms are incorporated into analytics and Consumer Relationships Management (CRM) stages to reveal information on how to better serve the customers. Chatbots have been integrated into websites to offer an immediate service to customers. Automation of job positions has gained recognition amongst the academics and IT analysts.
- AI in education : AI can automate grading, giving educators most of the time. It can assess students and adapt to their needs, enabling them to work at their own pace. AI tutors can offer extra support to students, guaranteeing that they stay on track. And it could change where and how students learn, perhaps even replacing some teachers.
- AI in finance : AI in personal finance applications, such as Intuit Mint or TurboTax, is upsetting the financial institutions. Applications such as these gather personal data and provide financial advice. Other programs like IBM Watson, have been applied to the process of purchasing a home. Today, artificial intelligence software performs much of the trading on Wall Street.
- AI in law : The discovery process — filtering through documents — in law is often overwhelming for humans. Using AI to help automate the legal industry’s labor-intensive processes is saving time and improving client service. Law firms are using machine learning to depict data and forsee outcomes, computer vision to categorise and extract information from documents and natural language processing to interpret requests for information.
- Transportation : Notwithstanding AI’s fundamental role in operating autonomous vehicles, AI technologies are used in transportation to manage traffic, predict flight delays, and make ocean shipping secure and more efficient.
- AI in security : AI and machine learning are at the top of the buzzword list security vendors use today to differentiate their offerings. Those terms also represent truly viable technology beiges. Artificial intelligence and machine learning in cybersecurity products are adding real value for security teams looking for ways to identify attacks, malware and other threats. Organizations use machine learning in security information and event management (SIEM) software and related areas to detect anomalies and identify suspicious activities that indicate threats. By analysing data and using logic to identify similarities to known malicious code, AI can provide alerts to new and emerging attacks much sooner than human employees and previous technology iterations.
- As a result, AI security technology both dramatically lowers the number of false positives and gives organizations more time to counteract real threats before damage is done. The maturing technology is playing a big role in helping organizations fight off cyberattacks.
AI is thus at the foxust of a new enterprise to build computational models of intelligence. The main assumption is that intelligence (human or otherwise) can be represented in terms of symbol structures and symbolic operations which can be programmed in a digital computer. There is much debate as to whether such an appropriately programmed computer would be a mind, or would merely simulate one, but AI researchers need not wait for the conclusion to that debate, nor for the hypothetical computer that could model all of human intelligence. Aspects of intelligent behaviour, such as solving problems, making inferences, learning, and understanding language, have already been coded as computer programs, and within very limited domains, such as identifying diseases of soybean plants, AI programs can outperform human experts. Now the great challenge of AI is to find ways of representing the common-sense knowledge and experience that enable people to carry out everyday activities such as holding a wide-ranging conversation, or finding their way along a busy street. Conventional digital computers may be capable of running such programs, or we may need to develop new machines that can support the complexity of human thought.