Many years ago when studying law at Dalhousie University in Halifax, Nova Scotia I heard a vibrant chap from the Nova Scotia College of Art and Design whimsically observe, “There’s nothing hard about law; you just need to know where to find it!”
Not everyone would be of the same opinion. But after a half-century career in the practice of law – and having developed an inter vivos trust agreement which contradicted some of the current opinions regarding the devolution and taxation of estates – I find I am readily disposed to alternate interpretation. The persuasion derives from a purely mechanical approach to analysis; that is, there’s nothing especially brilliant about it. The process is illustrative of the mere possibility of interpretation, one by the way which in this instance had the advantage of review and approbation by the late Mr. Justice James Knatchbull Hugessen of the Federal Court of Canada.
Born in Montreal in 1933, Hugessen was educated at the University of Oxford and McGill University. After graduating with a BCL from McGill in 1957, he was called to the bar in 1958 and entered private practice.
From 1962 to 1974, Hugessen was an adjunct professor in McGill’s Faculty of Law. In 1972, he was appointed a justice of the Quebec Superior Court. In 1983 he became a judge of the Federal Court of Canada, Appeal Division and retired in 2008.
In 2014, Hugessen was named a Member of the Order of Canada.
Hugessen died on April 21, 2024, in Almonte, Ontario, at the age of 90.
Locating variety of legal opinion is not surprisingly associated with what is now characterized as that vast assembly of knowledge called AI (Artificial Intelligence). While the resource (algorithm) isn’t guaranteed to offer an acceptable interpretation of the applicable law, it affords argument (which often is the best counsel can produce). If the supply of that intelligence is prompted by algorithm, I consider it a far stretch from plagiarism. Instead it is efficiency.
a process or set of rules to be followed in calculations or other problem-solving operations, especially by a computer.
“a basic algorithm for division”
In the result I have come to consider technology (the heading under which I associate AI) a superb convenience. After all, I witnessed the popular evolution of computers from fountain pens and typewriters – the most distinguished of which was the IBM Selectric memory typewriter (instantly and unceremoniously abandoned upon the appearance of the desktop computer and laser printer).
It is my expectation that textbooks and libraries may suffer a similar peril upon the functional advent of AI. Remember – computers first appeared as the video game Pac Man. Meanwhile there are those who devote themselves to worry about AI. My qualified use of AI leads me to conclude that, subject to obvious limitations, it can be an improvement. The judicial application of the tool is imperative – just as driving an automobile demands attention. And, yes, there will be accidents; but the utility outweighs the threat.
“How can we make sure that, in the K-12 context, that we’re equipping those kids, those students, with the skills that they’re going to need to be able to succeed in what we think of as the intelligence age?” Lehane said during the event. “And you can’t do that unless it’s actually given to the teachers to do that work.”
New YorkCNN — Meta CEO Mark Zuckerberg is personally assembling a team to achieve a “superintelligence,” machines that are capable of surpassing human capabilities, according to a Bloomberg report.
Zuckerberg’s superintelligence goal is extremely lofty. Before AI can achieve capabilities that outmatch humans’ brains, the technology first needs to become capable of accomplishing anything a human can do – a so-called artificial general intelligence. AI researchers debate how close we are to that AGI goal, with some saying we’re years away and other saying we’re nowhere close and we have no path to achieving it.
Nevertheless, the AI race is as competitive as any tech battle in recent memory. Meta is facing off with Microsoft-backed OpenAI and Alphabet, as well as a host of other major upstarts with serious funding, including Elon Musk’s xAI and Anthropic. Apple has gotten a slow start but announced some of its own AI developments this week.
Featured Photo from CNN