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Like AI

In this day and age with the likes of ChatGPT floating around, you are – at least in some way – utilising AI in your day-to-day and may not even know it.

The little unseen device that listens to your voice commands to play your favourite tune or find your easy-chicken-midweek dinner recipe. It’s such a great help when your hands are full and you’re trying to be productive.

You have probably read countless descriptions of products – especially in the legal tech world – saying they use AI in their product. Maybe you believe them, maybe you sign on with a service provider thinking you have every tech tick box ticked.

The thing is, would you really know if you didn’t?

It’s so easy to get mislead by all the buzzwords out there, that most of the time you are taking providers at their word. Not knowing any better yourself. This may be ok with the providers you know and can trust but what about the new guys on the block that promise everything and the kitchen sink? How can you tell the smooth operators from the “keep their promisers”?

One way is to become well acquainted with the buzzwords out there so that you can pick out the take-a-chance “sharks” from the noble (and trusted) workhorses.

And we thought we would help you out with understanding a few of those buzzwords yourself. So that you can decide for you.

Take AI as an example…

Let’s demystify AI

Artificial intelligence or AI is according to Builtin –

a wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence. While AI is an interdisciplinary science with multiple approaches, advancements in machine learning and deep learning, in particular, are creating a paradigm shift in virtually every sector of the tech industry.

Artificial intelligence allows machines to model, or even improve upon, the capabilities of the human mind. And from the development of self-driving cars to the proliferation of generative AI tools like ChatGPT and Google’s Bard, AI is increasingly becoming part of everyday life — and an area companies across every industry are investing in”.

And while that sounds easy enough to understand – smart machines capable of performing tasks that typically require human intelligence. Ok sort of easy to understand…

The thing is, understanding what AI is requires defining what intelligence is. And as argued by Relativity – how can we attempt to define AI? By defining intelligence itself? And if that’s the case, how is intelligence defined? A definition from Merriam-Webster sets out as follows –

the ability to apply knowledge to manipulate one’s environment or to think abstractly as measured by objective criteria (such as tests).

And if “tests” are used to define a machine’s intelligence then we are in luck because testing machines has been around since Alan Turing, the father of computer science, proposed it in 1950 – with what’s known as the Turing Test.

The test engages a machine in what Turing called an “Imitation Game”. A person would have a conversation with two participants using a computer keyboard and screen, one of the two participants is a machine, but the “tester” doesn’t know which one is the machine and which one is another human being. If the tester cannot tell which participant is the machine after five minutes of questioning, then it has passed the Turing Test (30% of the time was considered sufficient).

Naturally, with the Turing Test being 73 years old, there needed to be some new blood injected into the machine testing game.

In 2012, computer theorist Ben Goertzel proposed what he called the “robot university student test”. Goertzel argued that an AI capable of obtaining a degree in the same way as a human should be considered conscious. Goertzel’s idea might have remained a thought experiment were it not for the successes of AIs employing natural language processing (NLP): most famously, ChatGPT, the language model created by the OpenAI research laboratory (University World News).

Step in the coffee test. As set out in Medium 

In this age of ChatGPT, the Turing test seems quaint. Nobody would be fooled into thinking a computer is a person, regardless of how clever it might be, because it is after all just a computer. So, what’s next? The Coffee Test has been proposed, and is attributed to Steve Wozniak, one of the founders of Apple Computers. According to Steve, this test would require a robot, not a computer screen. The robot would need to locate the kitchen and brew a pot of coffee in a random house that it had never seen before. While the Turing Test is considered a test for artificial intelligence or AI, the Coffee Test is considered a test for artificial general intelligence, or AGI, which is sometimes defined as the ability of a machine to perform any task that a human can perform”.

The truth is, who is letting a random machine into their home to find the coffee? Despite the test being a Wozniak idea, it’s still pretty quaint.

While it’s difficult to truly define intelligence, the above tests do provide some metrics in order to start doing so. One thing is for certain especially keeping the above tests in mind – we are a far cry from actual artificial intelligence.

How does AI help us today?

Perhaps we shouldn’t be spending all this time on trying to define artificial intelligence. Perhaps splitting hairs over what intelligence means is pointless.

Truthfully, all you need to know is how AI is already being used today –


One of the most important uses of AI in the legal tech market today – automating repetitive and labour-intensive tasks. Something AJS offers in its product suites (compare them here).

Automation isn’t something new, we have already been using machines to automate dull, dirty, and dangerous tasks. Take automated bomb detection devices as one example.

Whether automation will eliminate jobs or create even more high-value jobs remains unseen. But just like the advent of ATMs didn’t see the end of bank tellers, we believe AI will simply support roles as opposed to replace them. Allowing lawyers to focus their attention on the more complex matters, we believe is the aim of the automation game.

Augmented creativity

While creativity remains a firmly human characteristic, artists are already using AI to create new works.

Early prototypes highlight the important role humans can, and should, play in making sense of the suggestions proposed by the AI. But the truth is, being able to create something new sometimes does necessitate different thinking. Sometimes it does involve the use of data. Sometimes it needs the interference of AI – with caution mind you.

OpenAI attempted to release a music-making tool called Jukebox. Here a machine learning framework generates music — including rudimentary songs — as raw audio in a range of genres and musical styles. Provided with a genre, artist, and lyrics as input, Jukebox outputs a new music sample produced from scratch. Jukebox might not be the most practical application of AI and machine learning, but as OpenAI notes, music generation pushes the boundaries of generative models (Game Changers).

Various projects have also attempted to produce new and enticing food recipes by using AI to mine food composition databases and concoct interesting combinations. For instance, Google researcher Sara Robinson recently showcased her system that produced a cake-cookie hybrid. Accenture researchers prototyped a similar recipe creation tool at their Dock facility in Dublin, but with stomach-churning results (Venture Beat).

Whether ultimately successful or not, using AI as inspiration for art remains the most prevalent artistic use case. Essentially, smart machines can accelerate creativity by serving as a kind of ‘creative arm’ that feeds and inspires creation.

Where is AI being used today?

AI in today’s world takes many forms, from chatbots to navigation apps and wearable fitness trackers. Some examples include –

  1. ChatGPT – a chatbot capable of producing written content in a range of formats, from essays to code and answers to simple questions.
  2. Google Maps – uses location data from smartphones, as well as user-reported data on things like construction and car accidents, to monitor the ebb and flow of traffic.
  3. Snapchat Filters – use ML algorithms to distinguish between an image’s subject and the background, track facial movements and adjust the image on the screen based on what the user is doing.
  4. Smart Assistants – like Siri and Alexa, this AI uses NLP to receive instructions from users to set reminders, search for online information and control the lights in people’s homes (Built in).

From the above, it’s clear that we have all used AI at some point in our everyday activities.

But the main takeaway from this article is this – when shopping around for your latest legal tech investment, don’t get hoodwinked by the overuse of technical buzzwords or empty promises of providing you the world – “Sure, we have AI” – because once you understand what the word means, you will know whether they are a smart investment or not.

(Sources used and to whom we owe thanks: Tech TargetRelativity BlogBuilt inMediumUniversity World NewsVenture Beat and Game Changers).

If you have any questions regarding the information we have set out above or if you have any queries relating to legal tech and how you can incorporate it into your practice, get-in-touch and let’s see how we can take your software solution from good to phenomenal.

If you don’t have any software supporting your legal practice yet, it’s not a problem. We are here to help you from scratch too.

AJS – as always – has your back!

– Written by Alicia Koch on behalf of AJS

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