Authored by Amit Aggarwal, co-founder and Director, Effectual Services

Every year with the growth in new technologies and inventions there have been an astounding growth in volume of intellectual property literature. Internationally, this data has to be gathered, stored, and classified in multiple formats and languages so that it can be used as and when required. However, data alone does not create a competitive advantage, extracting significant and actionable information from this data deluge represents a major challenge and an opportunity at the same time. Analysing patent documents from the pile of data manually is getting out of question day by day as it demands extensive time and resources. So, the examiners and patent analyst need all available tools at their disposal to perform this tedious task. One of the tools with a tremendous potential is Artificial Intelligence (AI). At its core, artificial intelligence is a computer that has been programmed to mimic the natural intelligence of human beings by learning, reasoning and making decisions.

From the days of fully constructed Boolean searches, search and analytics have evolved, thanks to AI-based semantic search algorithms to provide more efficient and accurate search result than ever before. A major advantage of artificial intelligence is its ability to provide repeated results as these systems are not hindered by inexperience or fatigue. Artificial intelligence tools have potential to significantly streamline and automate the patent search process and the increase the quality and speed of theobtaining results by reducing the amount of time examiners and analyst spend researching, for example,a prior art research project that can runs into days and weeks, can be performed by an AI tool in a matter of hours. Some existing tools, that are really advanced, also incorporate natural language based input that permitsa searcher to include natural language terms that can be comprehended by the backend artificial intelligence engine, which recovers comparable documents available in different languages.

The European Patent Office (EPO) uses Intelligent machine translation tool Patent Translate to allow for translation of patent publications from 32 languages into the EPO official languages of English, French and German. The US patent office (USPTO)uses artificial intelligence to help examiners to review pending patent applications by augmenting classification and searches currently a high priority with it. The UK patent office (UKIPO) also uses artificial intelligence solutions for prior art searching. IBM is offering Watson, an IP advisor that leverages artificial intelligence for fast patent ingestion, better insights, and analytics. Turbopatent, a company that develops applications to automate and streamline the patent protection process, has introduced two artificial intelligence products for patent lawyers. Roboreview, a cloud-based product that analyses drafted patent applications and rapid response, a product that assists lawyers in writing responses to office actions.

Many key players in the industry like PatSeer, Questel, have been using artificial intelligence in combination with machine learning & semantic-based algorithms to provide patent analytics tools and software.With the help of these tools and software we can now:

There are some opposing views relating to the implementation and benefit of artificial intelligence tools and techniques there are people who are concerned about the peculiarities of language used within patent documents, and doubt that how these tools can deal with the inherent ambiguities i.e.its lack of human reasoning as it is unable to carry out a sanity check of results or inventions and lacks the experience that leads to a persons intuitive response to situations.There have been some recorded incidents where the AI based tools failed to perform what it was intended to do.

All in all, its difficult to say whether the AI based tools will be able to completely mimic the human beings and perform same level of analysis or whether they will only reach to the extent of an additional help to a patent searcher we will see in coming times.

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Importance and Benefits of Artificial intelligence for Patent Searching - Express Computer

Artificial Intelligence is probably the most complex and astounding creations of humanity yet. And that is disregarding the fact that the field remains largely unexplored, which means that every amazing AI application that we see today represents merely the tip of the AI iceberg, as it were. While this fact may have been stated and restated numerous times, it is still hard to comprehensively gain perspective on the potential impact of AI in the future. The reason for this is the revolutionary impact that AI is having on society, even at such a relatively early stage in its evolution.

AIs rapid growth and powerful capabilities have made people paranoid about the inevitability and proximity of an AI takeover. Also, the transformation brought about by AI in different industries has made business leaders and the mainstream public think that we are close to achieving the peak of AI research and maxing out AIs potential. However, understanding the types of AI that are possible and the types that exist now will give a clearer picture of existing AI capabilities and the long road ahead for AI research.

Since AI research purports to make machines emulate human-like functioning, the degree to which an AI system can replicate human capabilities is used as the criterion for determining the types of AI. Thus, depending on how a machine compares to humans in terms of versatility and performance, AI can be classified under one, among the multiple types of AI. Under such a system, an AI that can perform more human-like functions with equivalent levels of proficiency will be considered as a more evolved type of AI, while an AI that has limited functionality and performance would be considered a simpler and less evolved type.

Based on this criterion, there are two ways in which AI is generally classified. One type is based on classifying AI and AI-enabled machines based on their likeness to the human mind, and their ability to think and perhaps even feel like humans. According to this system of classification, there are four types of AI or AI-based systems: reactive machines, limited memory machines, theory of mind, and self-aware AI.

These are the oldest forms of AI systems that have extremely limited capability. They emulate the human minds ability to respond to different kinds of stimuli. These machines do not have memory-based functionality. This means such machines cannot use previously gained experiences to inform their present actions, i.e., these machines do not have the ability to learn. These machines could only be used for automatically responding to a limited set or combination of inputs. They cannot be used to rely on memory to improve their operations based on the same. A popular example of a reactive AI machine is IBMs Deep Blue, a machine that beat chess Grandmaster Garry Kasparov in 1997.

Limited memory machines are machines that, in addition to having the capabilities of purely reactive machines, are also capable of learning from historical data to make decisions. Nearly all existing applications that we know of come under this category of AI. All present-day AI systems, such as those using deep learning, are trained by large volumes of training data that they store in their memory to form a reference model for solving future problems. For instance, an image recognition AI is trained using thousands of pictures and their labels to teach it to name objects it scans. When an image is scanned by such an AI, it uses the training images as references to understand the contents of the image presented to it, and based on its learning experience it labels new images with increasing accuracy.

Almost all present-day AI applications, from chatbots and virtual assistants to self-driving vehicles are all driven by limited memory AI.

While the previous two types of AI have been and are found in abundance, the next two types of AI exist, for now, either as a concept or a work in progress. Theory of mind AI is the next level of AI systems that researchers are currently engaged in innovating. A theory of mind level AI will be able to better understand the entities it is interacting with by discerning their needs, emotions, beliefs, and thought processes. While artificial emotional intelligence is already a budding industry and an area of interest for leading AI researchers, achieving Theory of mind level of AI will require development in other branches of AI as well. This is because to truly understand human needs, AI machines will have to perceive humans as individuals whose minds can be shaped by multiple factors, essentially understanding humans.

This is the final stage of AI development which currently exists only hypothetically. Self-aware AI, which, self explanatorily, is an AI that has evolved to be so akin to the human brain that it has developed self-awareness. Creating this type of Ai, which is decades, if not centuries away from materializing, is and will always be the ultimate objective of all AI research. This type of AI will not only be able to understand and evoke emotions in those it interacts with, but also have emotions, needs, beliefs, and potentially desires of its own. And this is the type of AI that doomsayers of the technology are wary of. Although the development of self-aware can potentially boost our progress as a civilization by leaps and bounds, it can also potentially lead to catastrophe. This is because once self-aware, the AI would be capable of having ideas like self-preservation which may directly or indirectly spell the end for humanity, as such an entity could easily outmaneuver the intellect of any human being and plot elaborate schemes to take over humanity.

The alternate system of classification that is more generally used in tech parlance is the classification of the technology into Artificial Narrow Intelligence (ANI), Artificial General Intelligence (AGI), and Artificial Superintelligence (ASI).

This type of artificial intelligence represents all the existing AI, including even the most complicated and capable AI that has ever been created to date. Artificial narrow intelligence refers to AI systems that can only perform a specific task autonomously using human-like capabilities. These machines can do nothing more than what they are programmed to do, and thus have a very limited or narrow range of competencies. According to the aforementioned system of classification, these systems correspond to all the reactive and limited memory AI. Even the most complex AI that uses machine learning and deep learning to teach itself falls under ANI.

Artificial General Intelligence is the ability of an AI agent to learn, perceive, understand, and function completely like a human being. These systems will be able to independently build multiple competencies and form connections and generalizations across domains, massively cutting down on time needed for training. This will make AI systems just as capable as humans by replicating our multi-functional capabilities.

The development of Artificial Superintelligence will probably mark the pinnacle of AI research, as AGI will become by far the most capable forms of intelligence on earth. ASI, in addition to replicating the multi-faceted intelligence of human beings, will be exceedingly better at everything they do because of overwhelmingly greater memory, faster data processing and analysis, and decision-making capabilities. The development of AGI and ASI will lead to a scenario most popularly referred to as the singularity. And while the potential of having such powerful machines at our disposal seems appealing, these machines may also threaten our existence or at the very least, our way of life.

At this point, it is hard to picture the state of our world when more advanced types of AI come into being. However, it is clear that there is a long way to get there as the current state of AI development compared to where it is projected to go is still in its rudimentary stage. For those holding a negative outlook for the future of AI, this means that now is a little too soon to be worrying about the singularity, and there's still time to ensure AI safety. And for those who are optimistic about the future of AI, the fact that we've merely scratched the surface of AI development makes the future even more exciting.

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7 Types Of Artificial Intelligence - Forbes

Among the myriad thematic exchange traded funds investors have to consider, artificial intelligence products are numerous and some are catching on with investors.

Count the ROBO Global Artificial Intelligence ETF THNQ, +1.57% as the latest member of the artificial intelligence ETF fray. HNQ, which debuted earlier this week, comes from a good gene pool as its stablemate,the Robo Global Robotics and Automation Index ETF ROBO, +0.44%, was the original and remains one of the largest robotics ETFs.

That's relevant because artificial intelligence and robotics are themes that frequently intersect with each other. Home to 72 stocks, the new THNQ follows the ROBO Global Artificial Intelligence Index.

Adding to the case for A.I., even with a new product such as THNQ, is that the technology has hundreds, if not thousands, of applications supporting its growth.

Companies developing AV technology are mainly relying on machine learning or deep learning, or both, according to IHS Markit. A major difference between machine learning and deep learning is that, while deep learning can automatically discover the feature to be used for classification in unsupervised exercises, machine learning requires these features to be labeled manually with more rigid rulesets. In contrast to machine learning, deep learning requires significant computing power and training data to deliver more accurate results.

Like its family ROBO, THNQ offers wide reach with exposure to 11 sub-groups. Those include big data, cloud computing, cognitive computing, e-commerce and other consumer angles and factory automation, among others. Of course, semiconductors are part of the THNQ fold, too.

The exploding use of AI is ushering in a new era of semiconductor architectures and computing platforms that can handle the accelerated processing requirements of an AI-driven world, according to ROBO Global. To tackle the challenge, semiconductor companies are creating new, more advanced AI chip engines using a whole new range of materials, equipment, and design methodologies.

While THNQ is a new ETF, investors may do well to not focus on that rather focus on the fact the AI boom is in its nascent stages.

Historically, the stock market tends to under-appreciate the scale of opportunity enjoyed by leading providers of new technologies during this phase of development, notes THNQ's issuer. This fact creates a remarkable opportunity for investors who understand the scope of the AI revolution, and who take action at a time when AI is disrupting industry as we know it and forcing us to rethink the world around us.

The new ETF charges 0.68% per year, or $68 on a $10,000 investment. That's inline with rival funds.

2020 Benzinga.com. Benzinga does not provide investment advice. All rights reserved.

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A New Way To Think About Artificial Intelligence With This ETF - MarketWatch

Watch Benjamin Alarie, co-founder and CEO of Blue J Legal, discuss the expanding role of artificial intelligence in tax with contributing editor atTax Notes FederalBenjamin Willis.

Here are some highlights

On machine learning and tax law

Benjamin Alarie: Whenwe talk about machine learning and artificial intelligence of the law, what we're doing is talking about collecting the raw materials, the rulings, the cases, the legislation, the regs, all that information, and bringing it to bear on a particular problem. We're synthesizing all of those materials to make a prediction about how a new situation would likely be decided by the courts.

. . . Law should be predictable. We have lots of data out there in the form of rulings, in the form of judgments that we can collect as good examples of how the courts have decided these matters in the past. And we can reverse engineer using machine learning methods how the courts are mapping the facts of different situations into outcomes. And we can do this in a really elegant way that leads to high quality prediction. So predictions of 90 percentor better accuracy about how the courts are going to make those decisions in the future, which is incredibly valuable to taxpayers to tax administration and to anyone who's looking for certainty, predictability and fairness, in the application of law.

On the availability of artificial intelligence

Benjamin Alarie: We're doing a lot to make this technology available throughout industry. Law firms are increasingly seeing this as one of the tools that they need to have in order to practice tax as effectively as possible. Academic programs see using this kind of technology [as]a huge boost for their graduates who are going to go into practice being familiar already with the leading tools for how to leverage machine learning and artificial intelligence. Accounting firms are also quite interested in this approach too because it has huge implications in terms of speeding up research [and] doing quality assurance . . .

On the moldability of results

Benjamin Alarie: You can play with different dimensions. You can swap out that assumption of fact, swap in a different assumption of fact, and see how that's likely to influence the results. So, then you can do scenario testing to really get comfortable with how much risk there is in a particular situation as the one providing a new opinion or providing advice to a client. That's really reassuring. You might say, Okay, I need to get this to 80 percent probability. I'm not willing to bite off more than that . . . Or you might be like, Well, I have a really risk-loving client. I just need to get to 51 percent . . . [machine learning] allows you to really calibrate the amount of risk that you're taking on, depending on the risk appetite of the client and your comfort as the practitioner.

Benjamin Willis, contributing editor with Tax Notes Federal, and Benjamin Alarie, co-founder and CEO ... [+] of Blue J Legal, discuss the expanding role of artificial intelligence and machine learning in the government, academia and tax practice.

On artificial intelligence and the courts

Benjamin Alarie: [Machine learning] is a great tool to encourage settlement between the parties, and so I think we're increasingly seeing that phenomenon where the party with the really strong position is using this to support their argument. They say, Don't take our word for it. We ran it on this independent system. . . Here's the report from the system saying that we have a 95 percent or better chance of winning this case. Are you still sure you don't want to enter into terms of settlement? That's often very convincing to the other side, who then run their analysis through the same system and they say, Okay . . . It's not nearly as strong as we thought it might be. Maybe we should talk about settling this and that saves judges from having to contend with cases that really aren't the best use of their time because it's pretty clear how those cases should get decided.

On artificial intelligence and low-income taxpayers

Benjamin Alarie: There are early adopters at these low income taxpayer clinics across the country who are interested in using technology to allow them to give faster advice to the low income taxpayers . . . Folks understand increasingly how to use the software and how it can materially assist their clientele and so the goal is to learn from those early adopters and to figure out how to position the software to help as much as possible in other clinics where maybe we don't have early adopters present, but who could still genuinely, really benefit from this.

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The Expanding Role Of Artificial Intelligence In Tax - Forbes

As far as a simple definition, Artificial Intelligence is the ability of a machine or a computer device to imitate human intelligence (cognitive process), secure from experiences, adapt to the most recent data and work people-like-exercises.

Artificial Intelligence executes tasks intelligently that yield in creating enormous accuracy, flexibility, and productivity for the entire system. Tech chiefs are looking for some approaches to implement artificial intelligence technologies into their organizations to draw obstruction and include values, for example, AI is immovably utilized in the banking and media industry. There is a wide arrangement of methods that come in the space of artificial intelligence, for example, linguistics, bias, vision, robotics, planning, natural language processing, decision science, etc. Let us learn about some of the major subfields of AI in depth.

ML is maybe the most applicable subset of AI to the average enterprise today. As clarified in the Executives manual for real-world AI, our recent research report directed by Harvard Business Review Analytic Services, ML is a mature innovation that has been around for quite a long time.

ML is a part of AI that enables computers to self-learn from information and apply that learning without human intercession. When confronting a circumstance wherein a solution is covered up in a huge data set, AI is a go-to. ML exceeds expectations at processing that information, extracting patterns from it in a small amount of the time a human would take and delivering in any case out of reach knowledge, says Ingo Mierswa, founder and president of the data science platform RapidMiner. ML powers risk analysis, fraud detection, and portfolio management in financial services; GPS-based predictions in travel and targeted marketing campaigns, to list a few examples.

Joining cognitive science and machines to perform tasks, the neural network is a part of artificial intelligence that utilizes nervous system science ( a piece of biology that worries the nerve and nervous system of the human cerebrum). Imitating the human mind where the human brain contains an unbounded number of neurons and to code brain-neurons into a system or a machine is the thing that the neural network functions.

Neural network and machine learning combinedly tackle numerous intricate tasks effortlessly while a large number of these tasks can be automated. NLTK is your sacred goal library that is utilized in NLP. Ace all the modules in it and youll be a professional text analyzer instantly. Other Python libraries include pandas, NumPy, text blob, matplotlib, wordcloud.

An explainer article by AI software organization Pathmind offers a valuable analogy: Think of a lot of Russian dolls settled within one another. Profound learning is a subset of machine learning and machine learning is a subset of AI, which is an umbrella term for any computer program that accomplishes something smart.

Deep learning utilizes alleged neural systems, which learn from processing the labeled information provided during training and uses this answer key to realize what attributes of the information are expected to build the right yield, as per one clarification given by deep AI. When an adequate number of models have been processed, the neural network can start to process new, inconspicuous sources of info and effectively return precise outcomes.

Deep learning powers product and content recommendations for Amazon and Netflix. It works in the background of Googles voice-and image-recognition algorithms. Its ability to break down a lot of high-dimensional information makes deep learning unmistakably appropriate for supercharging preventive maintenance frameworks

This has risen as an extremely sizzling field of artificial intelligence. A fascinating field of innovative work for the most part focuses around designing and developing robots. Robotics is an interdisciplinary field of science and engineering consolidated with mechanical engineering, electrical engineering, computer science, and numerous others. It decides the designing, producing, operating, and use of robots. It manages computer systems for their control, intelligent results and data change.

Robots are deployed regularly for directing tasks that may be difficult for people to perform consistently. Major robotics tasks included an assembly line for automobile manufacturing, for moving large objects in space by NASA. Artificial intelligence scientists are additionally creating robots utilizing machine learning to set interaction at social levels.

Have you taken a stab at learning another language by labeling the items in your home with the local language and translated words? It is by all accounts a successful vocab developer since you see the words again and again. Same is the situation with computers fueled with computer vision. They learn by labeling or classifying various objects that they go over and handle the implications or decipher, however, at a much quicker pace than people (like those robots in science fiction motion pictures).

The tool OpenCV empowers processing of pictures by applying them to mathematical operations. Recall that elective subject in engineering days called Fluffy Logic? Truly, that approach is utilized in Image processing that makes it a lot simpler for computer vision specialists to fuzzify or obscure the readings that cant be placed in a crisp Yes/No or True/False classification. OpenTLA is utilized for video tracking which is the procedure to find a moving object(s) utilizing a camera video stream.

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Five Important Subsets of Artificial Intelligence - Analytics Insight