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Technology jargon buster

Artificial intelligence is an area of computer science that is able to perform tasks that ordinarily require human intelligence and reaction. Some of the activities computers with artificial intelligence (AI) are designed for include: speech recognition, learning, planning, problem solving. (See also machine learning, expert systems and natural language processing).

Big data refers to very large data sets that may be analysed computationally to reveal patterns, trends and associations, especially relating to human behaviour and interactions.

Blockchain  is a decentralized and digital ledger that is used to record transactions across many computers, so the record cannot be altered without the collusion of the network. On a blockchain, transactions are recorded chronologically, forming an immutable chain, and can be private or anonymous depending on how the technology is implemented. The ledger is distributed across many participants in the network — it doesn’t exist in one place. Instead, copies exist and are simultaneously updated with every fully participating node in the ecosystem. This decentralised network will agree at regular intervals about the true state of shared data. This shared data can represent exchanges of currency, intellectual property, equity, information or other types of contracts and digital assets. It’s a general-purpose technology which can be used to trade scarce digital property rights and create novel types of platforms.

A blockchain facilitates irrefutable records of online transactions. Although originally developed for keeping track of online cryptocurrencies, there is widespread interest in blockchain’s alternative applications. This has led to the development of “Smart contracts”, which are contracts that are converted into code and stored on a blockchain. While there has not yet been a widespread adoption of Smart Contracts in the legal sector, this technology could revolutionise the future of contract management and transform contract-related disputes.

Blockchain is viewed by many as a potentially transformational software, with a wide range of professional and business applications. Potential use cases include: property ownership being transferred automatically upon receipt of cleared funds, securities could be traded without need for central securities depositories, ID management, KYC and AML, cross-border payments, supply chain management, royalty management and, smart contracts.

Collaboration tools – An online interface or dashboard between the professional firm and its clients.  These tools leverage knowledge management practices and offer opportunities for improved project management, sharing of documents, transparent communication, task management, and tracking fees.

Collaboration tools have been around in professional firms for many years. They have been developed as a direct result of investment into knowledge management strategies. They facilitate either internal collaboration across the law firm or connect the law firm to its client(s) or market.

They are especially beneficial in improving project management, improving transparency of actions to enhance client relationships at both personal and firm levels. However, it requires change in the way professionals work, integrating different elements of the project, and getting senior clients to log onto the system. Like most technologies it needs management and engagement to enable success.

Contract automation is a common application of document automation software. Document automation is a subset of the greater business process automation movement (BPM) within the enterprise software market. By reducing the time it takes to generate contracts and collateral, the expense of closing deals is reduced as well.

Data lake is a storage repository that holds a vast amount of raw data in its native format until it is needed. While a hierarchical data warehouse stores data in files or folders, a data lake uses a flat architecture to store data.

Expert systems emulate the decision-making ability of a human expert.  They are designed to solve complex problems by applying rules to known facts.  They then deduce new facts with enough rationale to explain to users how the decision was reached. Often used to explain simple rule-based computer solutions along the lines of “if this then that”. The system takes the rules of a task within a domain and hard codes them into the computer.

Expert systems were the first successful form of AI (starting in the 70s and used extensively in the 80s). You can see them in weather prediction, share prices and exchange rates. Expert systems have long been in use for professional services applications, such as self-service tools plus automated contract generation. They are well suited to high volumes and provide good economies of scale. They can be integrated with other applications such as electronic signatures and contract vaults. They are not suited to developing complex contracts or where substantial negotiation is needed.

Machine Learning is a field of computer science that uses statistical techniques to give computer systems the ability to learn by example from historical data to predict outcomes and uncover patterns that are not easily spotted by humans.

There are technical challenges in implementing Machine Learning, as they require more than just programming in rules. In very simplified terms, there are three main issues in developing Machine Learning for applications:

  1. Messiness of data
  2. Volumes of data
  3. Time investment in “training” the data

A Machine Learning system cannot predict things about it hasn’t been exposed to through previous input. Therefore, Machine Learning not only needs a lot of data (which is often not straightforward and hence the term “messy data”), but it also needs a way of making sense of the data. It does this by identifying, then coding features and attributes in the data. The machine learning system identifies uniqueness across the data set, reproducing what we learn naturally.  However, once Machine Learning is correctly set up it can be very effective.  Machine learning systems are bringing not only speed but accuracy to review work:

‘It searches right through the documentation, recall is very high, the chances of missing something are I think almost zero.’ (Director of Legal Services Innovation, Top 50 firm, B2B). Source: Law Society

Natural Language Processing refers to the interface between natural human speech or writing and a computer. This technology includes speech recognition, natural language processing and is moving onto generating natural language.  The background of Natural Language Processing (NLP) can be traced back to Alan Turing in the 1950s and relied heavily on machine learning to develop specific language-oriented algorithms; however, in recent years NLP has made rapid developments thanks to increases in computing power.

NLP has directly led to the development of Chatbots and virtual assistants. These have been used in a wide range of consumer applications and are now being adapted for use in the professional services sector.

NLP has the capability to carry out smart searches of existing regulation, cases and associated commentary, and to understand its meaning. Using smart search tools can cut back on manual time spent on research, leading to immediate efficiency savings.

Predictive analytics is the branch of advanced analytics which encompasses a variety of statistical techniques from data mining, predictive modelling and machine learning, which analyses current and historical facts to make predictions about future or otherwise unknown events.

Robotic Process Automation (RPA systems carry out repeatable tasks normally undertaken by either separate applications and/or by people. The tasks tend to cover queries, calculations, and transactions. RPA systems can be set up to manage multiple steps and turn them into an integral workflow such as taking received forms, sending receipts, filing the form, and updating the spreadsheet with the name of the form and so on.

Robotic process automation is becoming more prevalent in professional services. RPA automates standard processes, which in today’s professional firms are manifold. Client on-boarding; client due diligence (know your client); anti-money laundering, template contract generation, corporate secretarial, conveyancing, compliance work and conflict checks are just some of the processes dominating professionals’ time. RPAs reduce risk due to human error and can have a direct positive impact on compliance. The returns are quick and can significantly reduce the cost base.

Unsupervised machine learning algorithms infer patterns from a data set without reference to known or labelled outcomes and can be used for discovering the underlying structure of the data.