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Artificial Intelligence Technology

""Everything we love about civilization is a product of intelligence, so amplifying our human intelligence with artificial intelligence has the potential of helping civilization flourish like never before – as long as we manage to keep the technology beneficial.“

Artificial intelligence has changed the way we live with innovative technologies. AI has taken a storm in every industry and has a profound impact on every sector of society. The term Artificial intelligence terms were first coined in 1956 at a conference. The discussion of the conference led to interdisciplinary information tech natural language generationnology. The advent of the internet helped technology to progress exponentially. Artificial intelligence technology was a stand-alone technology thirty years, but now the applications are widespread in every sphere of life. Artificial intelligence is known by the AL acronym and is the process of recreating human intelligence in machines.

WHAT IS AI?

From SIRI to self-driving cars, artificial intelligence (AI) is progressing rapidly. While science fiction often portrays AI as robots with human-like characteristics, AI can encompass anything from Google’s search algorithms to IBM’s Watson to autonomous weapons.

Artificial intelligence today is properly known as narrow AI (or weak AI), in that it is designed to perform a narrow task (e.g. only facial recognition or only internet searches or only driving a car). However, the long-term goal of many researchers is to create general AI (AGI or strong AI). While narrow AI may outperform humans at whatever its specific task is, like playing chess or solving equations, AGI would outperform humans at nearly every cognitive task.

               WHY RESEARCH AI SAFETY?

In the near term, the goal of keeping AI’s impact on society beneficial motivates research in many areas, from economics and law to technical topics such as verification, validity, security and control. Whereas it may be little more than a minor nuisance if your laptop crashes or gets hacked, it becomes all the more important that an AI system does what you want it to do if it controls your car, your airplane, your pacemaker, your automated trading system or your power grid. Another short-term challenge is preventing a devastating arms race in lethal autonomous weapons.

In the long term, an important question is what will happen if the quest for strong AI succeeds and an AI system becomes better than humans at all cognitive tasks. As pointed out by I.J. Good in 1965, designing smarter AI systems is itself a cognitive task. Such a system could potentially undergo recursive self-improvement, triggering an intelligence explosion leaving human intellect far behind. By inventing revolutionary new technologies, such a superintelligenc

e might help us eradicate war, disease, and poverty, and so the creation of strong AI might be the biggest event in human history. Some experts have expressed concern, though, that it might also be the last unless we learn to align the goals of the AI with ours before it becomes superintelligent.

There are some who question whether strong AI will ever be achieved, and others who insist that the creation of superintelligent AI is guaranteed to be beneficial. At FLI we recognize both of these possibilities, but also recognize the potential for an artificial intelligence system to intentionally or unintentionally cause great harm. We believe research today will help us better prepare for and prevent such potentially negative consequences in the future, thus enjoying the benefits of AI while avoiding pitfalls.

HOW CAN AI BE DANGEROUS?

Most researchers agree that a superintelligent AI is unlikely to exhibit human emotions like love or hate and that there is no reason to expect AI to become intentionally benevolent or malevolent. Instead, when considering how AI might become a risk, experts think two scenarios most likely:

1. The AI is programmed to do something devastating: Autonomous weapons are artificial intelligence systems that are programmed to kill. In the hands of the wrong person, these weapons could easily cause mass casualties. Moreover, an AI arms race could inadvertently lead to an AI war that also results in mass casualties. To avoid being thwarted by the enemy, these weapons would be designed to be extremely difficult to simply “turn off,” so humans could plausibly lose control of such a situation. This risk is one that’s present even with narrow AI, but grows as levels of AI intelligence and autonomy increase.
2.  The AI is programmed to do something beneficial, but it develops a destructive method for achieving its goal: This can happen whenever we fail to fully align the AI’s goals with ours, which is strikingly difficult. If you ask an obedient intelligent car to take you to the airport as fast as possible, it might get you there chased by helicopters and covered in vomit, doing not what you wanted but literally what you asked for. If a superintelligent system is tasked with a ambitious geoengineering project, it might wreak havoc with our ecosystem as a side effect, and view human attempts to stop it as a threat to be met.

As these examples illustrate, the concern about advanced AI isn’t malevolence but competence. A super-intelligent AI will be extremely good at accomplishing its goals, and if those goals aren’t aligned with ours, we have a problem. You’re probably not an evil ant-hater who steps on ants out of malice, but if you’re in charge of a hydroelectric green energy project and there’s an anthill in the region to be flooded, too bad for the ants. A key goal of AI safety research is to never place humanity in the position of those ants.

WHY THE RECENT INTEREST IN AI SAFETY

Stephen Hawking, Elon Musk, Steve Wozniak, Bill Gates, and many other big names in science and technology have recently expressed concern in the media and via open letters about the risks posed by AI, joined by many leading AI researchers. Why is the subject suddenly in the headlines?

The idea that the quest for strong AI would ultimately succeed was long thought of as science fiction, centuries or more away. However, thanks to recent breakthroughs, many AI milestones, which experts viewed as decades away merely five years ago, have now been reached, making many experts take seriously the possibility of superintelligence in our lifetime. While some experts still guess that human-level AI is centuries away, most AI researchers at the 2015 Puerto Rico Conference guessed that it would happen before 2060. Since it may take decades to complete the required safety research, it is prudent to start it now.

Because AI has the potential to become more intelligent than any human, we have no surefire way of predicting how it will behave. We can’t use past technological developments as much of a basis because we’ve never created anything that has the ability to, wittingly or unwittingly, outsmart us. The best example of what we could face may be our own evolution. People now control the planet, not because we’re the strongest, fastest, or biggest, but because we’re the smartest. If we’re no longer the smartest, are we assured to remain in control?

FLI’s position is that our civilization will flourish as long as we win the race between the growing power of technology and the wisdom with which we manage it. In the case of AI technology, FLI’s position is that the best way to win that race is not to impede the former but to accelerate the latter, by supporting AI safety research.

Latest Artificial Intelligence Technologies 

1. Natural language generation

Machines process and communicate in a different way than the human brain. Natural language generation is a trendy technology that converts structured data into the native language. The machines are programmed with algorithms to convert the data into a desirable format for the user. Natural language is a subset of artificial intelligence that helps content developers to automate content and deliver in the desired format. The content developers can use the automated content to promote on various social media platforms, and other media platforms to reach the targeted audience. Human intervention will significantly reduce as data will be converted into desired formats. The data can be visualized in the form of charts, graphs, etc.

2. Speech recognition

Speech recognition is another important subset of artificial intelligence that converts human speech into a useful and understandable format by computers. Speech recognition is a bridge between human and computer interactions. The technology recognizes and converts human speech in several languages. Siri of iPhone is a                                   classic example of speech recognition.

3. Virtual agents

Virtual agents have become valuable tools for instructional designers. A virtual agent is a computer application that interacts with humans. Web and mobile applications provide chatbots as their customer service agents to interact with humans to answer their queries. Google Assistant helps to organize meetings, and Alexia from Amazon helps to make your shopping easy. A virtual assistant also acts like a language assistant, which picks cues from your choice and preference. The IBM Watson understands the typical customer service queries which are asked in several ways. Virtual agents act as software-as-a-service too

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4. Decision management

Modern organizations are implementing decision management systems for data conversion and interpretation into predictive models. Enterprise-level applications implement decision management systems to receive up-to-date information to perform business data analysis to aid in organizational decision-making. Decision management helps in making quick decisions, avoidance of risks, and in the automation of the process. The decision management system is widely implemented in the financial sector, the health care sector, trading, insurance sector, e-commerce, etc.

5. Biometrics

Deep learning another branch of artificial intelligence that functions based on artificial neural networks. This technique teaches computers and machines to learn by example just the way humans do. The term “deep” is coined because it has hidden layers in neural networks. Typically, a neural network has 2-3 hidden layers and can have a maximum of 150 hidden layers. Deep learning is effective on huge data to train a model and a graphic processing unit. The algorithms work in a hierarchy to automate predictive analytics. Deep learning has spread its wings in many domains like aerospace and military to detect objects from satellites, helps in improving worker safety by identifying risk incidents when a worker gets close to a machine, helps to detect cancer cells, etc.

6. Machine learning

Machine learning is a division of artificial intelligence which empowers machine to make sense from data sets without being actually programmed. Machine learning technique helps businesses to make informed decisions with data analytics performed using algorithms and statistical models. Enterprises are investing heavily in machine learning to reap the benefits of its application in diverse domains. Healthcare and the medical profession need machine learning techniques to analyze patient data for the prediction of diseases and effective treatment. The banking and financial sector needs machine learning for customer data analysis to identify and suggest investment options to customers and for risk and fraud prevention. Retailers utilize machine learning for predicting changing customer preferences, consumer behavior, by analyzing customer data.

7. Robotic process automation

Robotic process automation is an application of artificial intelligence that configures a robot (software application) to interpret, communicate and analyze data. This discipline of artificial intelligence helps to automate partially or fully manual operations that are repetitive and rule-based.

8. Peer-to-peer network

The peer-to-peer network helps to connect between different systems and computers for data sharing without the data transmitting via server. Peer-to-peer networks have the ability to solve the most complex problems. This technology is used in cryptocurrencies. The implementation is cost-effective as individual workstations are                             connected and servers are not installed.

9. Deep learning platforms

Deep learning another branch of artificial intelligence that functions based on artificial neural networks. This technique teaches computers and machines to learn by example just the way humans do. The term “deep” is coined because it has hidden layers in neural networks. Typically, a neural network has 2-3 hidden layers and can have a maximum of 150 hidden layers. Deep learning is effective on huge data to train a model and a graphic processing unit. The algorithms work in a hierarchy to automate predictive analytics. Deep learning has spread its wings in many domains like aerospace and military to detect objects from satellites, helps in improving worker safety by identifying risk incidents when a worker gets close to a machine, helps to detect cancer cells, etc.

10. AL optimized hardware

Artificial intelligence software has a high demand in the business world. As the attention for the software increased, a need for the hardware that supports the software also arise. A conventional chip cannot support artificial intelligence models. A new generation of artificial intelligence chips is being developed for neural networks, deep learning, and computer vision. The AL hardware includes CPUs to handle scalable workloads, special purpose built-in silicon for neural networks, neuromorphic chips, etc. Organizations like Nvidia, Qualcomm. AMD is creating chips that can perform complex AI calculations. Healthcare and automobile may be the industries that will benefit from these chips.

How artificial intelligence is transforming the future of healthcare one step at a time

Current use cases are already exhibiting AI’s transformational impact in healthcare and future potential uses offer astonishing possibilities.

Here are some broad use case scenarios for current AI use:

Improving Diagnostics: It is one of AI's most exciting healthcare applications.

Drug Discovery: AI is being harnessed to identify new therapies from vast databases of information on existing medicines. 

Predictive Patient Risk Identification: At-risk patients can be swiftly identified by algorithmic analysis of vast amounts of historic patient data.

Primary Care: AI-based voice-to-text technologies save countless hours taken to type memos. 

AI Robot-Assisted Surgery: It is another area that is being explored to help with everything from minimally-invasive procedures to open-heart surgery. Working with doctors, robots have already been able to carry out complex procedures successfully with precision, flexibility, and control that goes beyond human capabilities.

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