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:

• Search prior art and analyse from a large number of patent families in multiple languages
• Analyse citations, search for assignees, inventors and perform design searches directly 
• Tracing patent office actions or payment of maintenance fees
• Removing un-patentable inventions up front, maintaining and parsing patent portfolios and reducing the patent sample space to search from available pile.

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 person’s 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, it’s 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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From personal assistants like Siri, to movie suggestions on Netflix, artificial intelligence (NYSE:AI) is rapidly becoming ubiquitous in everyday life. As this technology continues to advance in capability and prevalence, we sought to explore AI and several closely related subtopics: machine leaning, deep learning, and neural networks.

What are the Differences between Artificial Intelligence, Machine Learning, and Deep Learning?

While artificial intelligence (AI), machine learning (ML), and Deep Learning (NYSE:DL) are often used interchangeably, there are several key differences. One way to visualize the relationship is through a series of concentric circles. AI is the macro topic which encompasses the entire field of study, while ML is a subtopic within AI. DL is a further refinement of ML and represents the most cutting edge of AI applications that are being used today.

At a basic level, artificial intelligence is the concept of machines accomplishing tasks which have historically required human intelligence.1 AI can be broken down into two distinct fields:

Applied AI: Machines designed to complete very specifics tasks like navigating a vehicle, trading stocks, or playing chess – as IBM’s Deep Blue demonstrated in 1996 when it defeated chess grand master Gerry Kasparov.

General AI: Machines designed to complete any task which would normally require human intervention. The broad nature of General AI requires machines to “learn” as they encounter new tasks or situations. This need for a learned approach is what gave rise to modern Machine Learning.

Today, many firms at the cutting edge of AI are focusing on machine learning (ML). In simple terms, ML is the process of building machines which can access data, apply algorithms to this data, and then train themselves to deduce valuable insights based on these underlying datasets.

The key difference between ML and AI is that ML does not rely explicitly on the code of its creator. Rather, ML systems use computer code as a starting point and then gather data, information, and inputs which can be studied – similar to how a student might study for an exam. It is this relationship with big data that makes ML and the Internet of Things(connecting regular objects to the internet so they can collect data or be controlled remotely) so closely intertwined.

Currently, ML is typically used to recognize faces, voice commands, and objects, as well as to translate languages. It has been successfully implemented in chatbots, such as Siri (Apple), Cortana (Microsoft), and Alexa (Amazon). With the victory of Google’s “Deep Mind” over the Go world champion in 2016, ML is now increasingly becoming accepted as a useful tool for decision making in the corporate world.

Deep learning takes artificial intelligence a step further, by mimicking how the human brain works through the use of artificial neural networks. In an artificial neural network, each neuron is charged with providing a binary (yes/no) response to basic questions about a piece of data. By layering thousands (or millions) of these artificial neural networks, a Deep Learning machine is able to generate reliable outputs (recommendations or interactions) without changing the underlying coding.

Consider a very basic artificial neural network which is responsible for determining if a photo contains a banana or an apple. The network has three neurons which are responsible for answering:

1.  Is the object in the picture round?
2. Is the object in the picture yellow?
3.  Does the object in the picture have a stem?

The network would respond with no, yes, no for the photo of a banana and yes, no, yes for the photo of an apple. Using binary, the network would learn that a banana is 010 and an apple is 101. Extrapolate this concept across thousands of yes/no questions of exponential complexity and you have the bases of artificial neural networks and deep learning.

Apart from being used in image and voice recognition algorithms, companies are implementing deep learning to predict customer preferences, detect fraud and spam, fight malware, conduct life-saving diagnoses, and recognize handwriting. In many ways, the possibilities for this technology are endless.

What’s Ahead?

Gartner’s recent study projects that by the end of this decade, the average person will have more conversations with a virtual assistant or bot than with his or her immediate family.6Such penetration of artificial intelligence into our everyday lives will depend on further advances in the technology to become smarter, more capable, and easier to interact with. While many expect this progress to occur from advances in machine learning and deep learning, there are new techniques to being introduced as well.

It’s with this momentum in mind that we developed our Robotics and Artificial Intelligence ETF (NASDAQ:BOTZ). The fund seeks to invest in companies that can potentially benefit from increased adoption and utilization of robotics and artificial intelligence.

1. https://www.forbes.com/sites/bernardmarr/2016/12/06/what-is-the-difference-between-artificial-intelligence-and-machine-learning/#35aa37802742

2. https://www.leverege.com/blogpost/the-difference-between-artificial-intelligence-machine-learning-and-deep-learning

3. http://www.techrepublic.com/article/machine-learning-the-smart-persons-guide/

4. http://www.techrepublic.com/article/7-companies-that-used-machine-learning-to-solve-real-business-problems/

5. http://www.explainthatstuff.com/introduction-to-neural-networks.html

6. http://www.gartner.com/smarterwithgartner/gartner-predicts-a-virtual-world-of-exponential-change/

Investing involves risk, including the possible loss of principal. The investable universe of companies in which BOTZ may invest may be limited. The Fund invests in securities of companies engaged in Information Technology which can be affected by rapid product obsolescence, and intense industry competition. In addition to normal risks associated with investing, international investments may involve risk of capital loss from unfavorable fluctuation in currency values, from differences in generally accepted accounting principles or from social, economic or political instability in other nations. The fund is non-diversified which represents a heightened risk to investors.

Shares are bought and sold at market price (not NAV) and are not individually redeemed from the Fund. Brokerage commissions will reduce returns.

Carefully consider the Fund’s investment objectives, risk factors, charges and expenses before investing. This and additional information can be found in the Fund’s full or summary prospectus, which may be obtained by calling 1-888-GX-FUND-1 (1.888.493.8631), or by visiting globalxfunds.com. Read the prospectus carefully before investing.

Global X Management Company LLC serves as an advisor to Global X Funds. The Funds are distributed by SEI Investments Distribution Co. (SIDCO), which is not affiliated with Global X Management Company LLC. Global X Funds are not sponsored, endorsed, issued, sold or promoted by Solactive AG, FTSE, Standard & Poors, NASDAQ, Indxx, or MSCI nor do these companies make any representations regarding the advisability of investing in the Global X Funds. Neither SIDCO nor Global X is affiliated with Solactive AG, FTSE, Standard & Poors, NASDAQ, Indxx, or MSCI.

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