Tag Archives: #machinelearning

AI & Music

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In a recent episode of a TV detective show, an AI tech dude tries to outsmart an old school musicologist by re-creating the missing part of a vintage blues recording. The professor is asked to identify which is the “real” track, compared to the AI versions. The blues expert guesses correctly within a few beats – much to the frustration of the coder.

“How did you figure it out so quickly?”

“Easy – it’s not just what the AI added, but more importantly what it left out.”

The failure of AI to fully replicate the original song (by omitting a recording error that the AI has “corrected”) is another example showing how AI lacks the human touch, does not yet have intuition, and struggles to exercise informed judgement. Choices may often be a matter of taste, but innate human creativity cannot yet be replicated.

Soon, though, AI tools will displace a lot of work currently done by composers, lyricists, musicians, producers, arrangers and recording engineers. Already, digital audio workstation (DAW) software easily enables anyone with a computer or mobile device to create, record, sample and mix their own music, without needing to read a note of music and without having to strum a chord. Not only that, the software can emulate the acoustic properties of site-specific locations, and correct out-of-tune and out-of-time recordings. So anyone can pretend they are recording at Abbey Road.

I recently blogged about how AI is presenting fresh challenges (as well as opportunities) for the music industry. Expect to see “new” recordings released by (or attributed to) dead pop stars, especially if their back catalogue is out of copyright. This is about more than exhuming preexisting recordings, and enhancing them with today’s technology; this is deriving new content from a set of algorithms, trained on vast back catalogues, directed by specific prompts (“bass line in the style of Jon Entwistle”), and maybe given some core principles of musical composition.

And it’s the AI training that has prompted the major record companies to sue two AI software companies, a state of affairs which industry commentator, Rob Abelow says was inevitable, because:

“It’s been clear that Suno & Udio have trained on copyrighted material with no plan to license or compensate”.

But on the other hand, streaming and automated music are not new. Sound designer and artist Tero Parviainen recently quoted Curtis Roads’ “The Computer Music Tutorial” (2023):

“A new industry has emerged around artificial intelligence (AI) services for creating generic popular music, including Flow Machines, IBM Watson Beat, Google Magenta’s NSynth Super, OpenAI’s Jukebox, Jukedeck, Melodrive, Spotify’s Creator Technology Research Lab, and Amper Music. This is the latest incarnation of a trend that started in the 1920s called Muzak, to provide licensed background music in elevators, business and dental offices, hotels, shopping malls, supermarkets, and restaurants”

And even before the arrival of Muzak in the 1920s, the world’s first streaming service was launched in the late 1890s, using the world’s first synthesizer – the Teleharmonium. (Thanks to Mark Brend’s “The Sound of Tomorrow”, I learned that Mark Twain was the first subscriber.)

For music purists and snobs (among whom I would probably count myself), all this talk about the impact of AI on music raises questions of aesthetics as well as ethics. But I’m reminded of some comments made by Pink Floyd about 50 years ago, when asked about their use of synthesizers, during the making of “Live at Pompeii”. In short, they argue that such machines still need human input, and as long as the musicians are controlling the equipment (and not the other way around), then what’s the problem? It’s not like they are cheating, disguising what they are doing, or compensating for a lack of ability – and the technology doesn’t make them better musicians, it just allows them to do different things:

“It’s like saying, ‘Give a man a Les Paul guitar, and he becomes Eric Clapton… It’s not true.'”

(Well, not yet, but I’m sure AI is working on it…)

Next week: Some final thoughts on AI

BYOB (Bring Your Own Brain)

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My Twitter and LinkedIn feeds are full of posts about artificial intelligence, machine learning, large language models, robotics and automation – and how these technologies will impact our jobs and our employment prospects, often in very dystopian tones. It can be quite depressing to trawl through this material, to the point of being overwhelmed by the imminent prospect of human obsolescence.

No doubt, getting to grips with these tools will be important if we are to navigate the future of work, understand the relationship between labour, capital and technology, and maintain economic relevance in a world of changing employment models.

But we have been here before, many times (remember the Luddites?), and so far, the human condition means we learn to adapt in order to survive. These transitions will be painful, and there will be casualties along the way, but there is cause for optimism if we remember our post-industrial history.

First, among recent Twitter posts there was a timely reminder that automation does not need to equal despair in the face of displaced jobs.

Second, the technology at our disposal will inevitably make us more productive as well as enabling us to reduce mundane or repetitive tasks, even freeing up more time for other (more creative) pursuits. The challenge will be in learning how to use these tools, and in efficient and effective ways so that we don’t swap one type of routine for another.

Third, there is still a need to consider the human factor when it comes to the work environment, business structures and organisational behaviour – not least personal interaction, communication skills and stakeholder management. After all, you still need someone to switch on the machines, and tell them what to do!

Fourth, the evolution of “bring your own device” (and remote working) means that many of us have grown accustomed to having a degree of autonomy in the ways in which we organise our time and schedule our tasks – giving us the potential for more flexible working conditions. Plus, we have seen how many apps we use at home are interchangeable with the tools we use for work – and although the risk is that we are “always on”, equally, we can get smarter at using these same technologies to establish boundaries between our work/life environments.

Fifth, all the technology in the world is not going to absolve us of the need to think for ourselves. We still need to bring our own cognitive faculties and critical thinking to an increasingly automated, AI-intermediated and virtual world. If anything, we have to ramp up our cerebral powers so that we don’t become subservient to the tech, to make sure the tech works for us (and not the other way around).

Adopting a new approach means:

  • not taking the tech for granted
  • being prepared to challenge the tech assumptions (and not be complicit in its in-built biases)
  • question the motives and intentions of the tech developers, managers and owners (especially those of known or suspected bad actors)
  • validate all the newly-available data to gain new insights (not repeat past mistakes)
  • evaluate the evidence based on actual events and outcomes
  • and not fall prey to hyperbolic and cataclysmic conjectures

Finally, it is interesting to note the recent debates on regulating this new tech – curtailing malign forces, maintaining protections on personal privacy, increasing data security, and ensuring greater access for those currently excluded. This is all part of a conscious narrative (that human component!) to limit the extent to which AI will be allowed to run rampant, and to hold tech (in all its forms) more accountable for the consequences of its actions.

Next week: “The Digital Director”

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Startup Vic’s EdTech Pitch Night

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EdTech or EduTech? Even Startup Vic can’t seem to decide. Whatever, this education-themed pitch night was the latest event in their highly popular monthly events, held in conjunction with Education Changemakers, and EduGrowth.

Apart from the naming convention, there is also some clarification needed around the scope and definition of “education(al) technology”. First, because it’s a very broad spectrum (does it include e-learning, e-books, MOOCS, LMS?). Second, is it more about the “delivery” than “outcomes”? Third, is it only about formal pedagogy, or does it also include discretionary, self-directed and non-curriculum learning?

And so to the pitches, in the order they presented:

Become

With the aim of “teaching kids to explore, design and navigate their future“, Become is essentially a platform for early-stage career coaching. While their app is still in development (although there is a bot in use already?), Become has been running in-person workshops and other programs to test and validate the concept. The solution uses AI and machine learning technology, but it wasn’t very clear how this will actually work – maybe there are some core profiling and preference tools, some career mapping based on proprietary algorithms, and recommendation engines drawing on the data analysis?

Using a freemium model, the full service will cost $40 per student per annum. The core audience are years 5 to 8, and part of the schools adoption strategy will focus on getting high school career advisers on-board, with additional parent advocacy.

I’ve no doubt that career advice is an important part of the syllabus, but just as important are life-long learning, resilience, adaptability, and developing self-awareness and a sense of purpose. But if nothing else, in the words of the founder, Become puts the “why” back into learning.

MoxieReader

This digital reading log is all about “inspired independent reading“. Supplementing the paper-based records widely in use, the app enables children to record their reading activity, and helps teachers to assess pupils’ reading progress, based on the titles and numbers of books read, and their associated word counts and vocabulary. (In future, the app may deliver content and instructional aids.)

Using a machine learning algorithm (“like a fitness tracker”), the app can set reading challenges, and measure reading growth. Tests may be another add-on, but from what I can see, the app does not test for comprehension or context-based reading and interpretation skills. (After all “reasoning” is the 4th “R” of education – along with reading, writing and arithmetic.)

Currently launching with an ambitious social media and outreach campaign, MoxieReader already has paid sign ups from teachers, many of whom are paying with their personal credit card, and is enjoying a 30% conversion rate, and 30% referral business.

Priced at $7 for teachers per class per month, plus $100 per school/building per month (individual teachers who already subscribed will get a rebate), there is also an opt-in donation model for parents to recycle used books.

Cogniss

This is a development platform and market place for education apps. Built on game based learning and rewards packages, it also makes use of analytics and data insights to help teachers and designers build their own products.

Having seen a demand among health and well-being users, the platform is also suited for apps designed to support behavioral change, workplace learning and social learning.

Access to the platform involves a $500 set up fee, plus $50 per month per app (plus scale rates by number of users and advanced add-ons).

The platform also supports micro-transactions, for downloaded content and apps. At present, there is no formal process for teachers to embed pedagogy into the game structure. Content vetting is also a manual process, combined with experience sharing and peer ratings – but a content certification process is in the pipeline.

Revision Village

Helping students to prepare for external exams (specifically, the IB maths) this product replaces traditional in person and in class programs, with an online resource.
Also, although revision practice largely relies on past test papers, the founders have identified a chasm between the concepts taught, and the questions asked.

Developed in response to teacher demand, this subscription-based learning resource has
translated into higher results and fewer fails.

The platform is looking to extend the curriculum beyond maths, but this will largely depend on being able to license content from the relevant examination boards and syllabus providers, such as the IB.

Access is not dependent upon being logged into a school network or intranet, as it is only a web app (with individual and site licenses).

The Revision Village website claims the product is used by “More than 32,000 IB Students and 710 IB Schools”. However, it would seem that not all of these are paid-for subscriptions, as the pitch mentioned a critical mass would be 100 schools (out of a total of 2,500 IB schools) paying $2,000 each (although this is separate to the parent market).

 

Overall, I liked the tone and format of the pitches –  the products all seemed worthy endeavours, and the founders are no doubt passionate about education and learning. But I was left feeling underwhelmed, by both the content and the tech being deployed. (I guess I needed more than just passing references to “AI, machine learning and algorithms”.) All of these products rely on significant adoption rates among schools – which are some of the hardest institutional customers to sell to – and to be successful in international markets presents a further challenge, given differences of language, content and educational systems.

In the end, even the judges found it hard to pick a winner, as there was a tie for 1st place, between Become and MoxieReader. I would probably concur, as they had the edge in terms of both individual learning outcomes, and broader educational benefits.

Next week: Copyright – Use It Or Lose It?

When robots say “Humans do not compute…”

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There’s a memorable scene in John Carpenter‘s 1970’s sci-fi classic, “Dark Star” where an astronaut tries to use Cartesian Logic to defuse a nuclear bomb. The bomb is equipped with artificial intelligence and is programmed to detonate via a timer once its circuits have been activated. Due to a circuit malfunction, the bomb believes it has been triggered, even though it is still attached to the spaceship, and cannot be mechanically released. Refuting the arguments against its existence, the bomb responds in kind, and simply states: “I think, therefore I am.”

Dark Star’s Bomb 20: “I think, therefore I am…”

Dark Star’s Bomb 20: “I think, therefore I am…”

The notion of artificial intelligence both thrills us, and fills us with dread: on the one hand, AI can help us (by doing a lot of routine thinking and mundane processing); on the other, it can make us the subjects of its own ill-will (think of HAL 9000 in “2001: A Space Odyssey”, or “Robocop”, or “Terminator” or any similar dystopian sci-fi story).

The current trend for smarter data processing, fuelled by AI tools such as machine learning, semantic search, sentiment analysis and social graph models, is making a world of driverless cars, robo-advice, the Internet of Things and behaviour prediction a reality. But there are concerns that we will abnegate our decision-making (and ultimately, our individual moral responsibility) to computers; that more and more jobs will be lost to robots; and we will end up being dehumanized if we no longer have to think for ourselves. Worse still, if our human behaviours cease making sense to those very same computers that we have programmed to learn how to think for us, then our demise is pre-determined.

The irony is, that if AI becomes as smart as we might imagine, then we will impart to the robots a very human fallibility: namely, the tendency to over-analyse the data (rather than examine the actual evidence before us). As Brian Aldiss wrote in his 1960 short story, “The Sterile Millennia”, when robots get together:

“…they suffer from a trouble which sometimes afflicts human gatherings: a tendency to show off their logic at the expense of the object of the meeting.”

Long live logic, but longer still live common sense!

Next week: 101 #Startup Pitches – What have we learned?