Introducing his students to the study of the human brain Jeff Lichtman, a Harvard Professor of Molecular and Cellular Biology, once asked: “If understanding everything you need to know about the brain was a mile, how far have we walked?”. He received answers like ‘three-quarters of a mile’, ‘half a mile’, and ‘a quarter of a mile’.
The professor’s response?: “I think about three inches.”
Last month, Lichtman’s quip made it into the pages of a new report by the Royal Society which examines the prospects for neural (or “brain-computer”) interfaces, a hot research area that has seen billions of dollars of funding plunged into it over the last few years, and not without cause. It is projected that the worldwide market for neurotech products – defined as “the application of electronics and engineering to the human nervous system” – will reach as much as $13.3 billion by 2022.
Sarah Bernhardt plays Hamlet, London 1899
“What’s Hecuba to him, or he to Hecuba,
That he should weep for her?”
The close of Act II Scene ii, and Hamlet questions how the performers in a play about the siege of Troy are able to convey such emotion – feel such empathy – for the stranger queen of an ancient city.
The construct here is complex. A play within a play, sparking a key moment of introspection, and ultimately self doubt. It is no coincidence that in this same work we find perhaps the earliest use of the term “my mind’s eye,” heralding a shift in theatrical focus from traditions of enacted disputes, lovers passions, and farce, to more a more nuanced kind of drama that issues from psychological turmoil.
Hamlet is generally considered to be a work of creative genius. For many laboring in the creative arts, works like this and those in its broader category serve as aspirational benchmarks. Indelible reminders of the brilliant outlands of human creativity.
Now, for the first time in our history, humans have a rival in deliberate acts of aesthetic creation. In the midst of the avalanche of artificial intelligence hype comes a new promise – creative AI; here to relieve us of burdensome tasks including musical, literary, and artistic composition.
Last month, Oscar Schwartz wrote a byline for OneZero with a familiarly provocative headline: “What If an Algorithm Could Predict Your Unborn Child’s Intelligence?”. The piece described the work of Genomic Prediction, a US company using machine learning to pick through the genetic data of embryos to establish the risk of health conditions. Given the title of the article, the upshot won’t surprise you. Prospective parents can now use this technology to expand their domain over the “design” of new offspring – and “cognitive ability” is among the features up for selection.
Setting aside the contention over whether intelligence is even inheritable, the ethical debate around this sort of pre-screening is hardly new. Gender selection has been a live issue for years now. Way back in 2001, Oxford University bioethicist Julian Savulescu caused controversy by proposing a principle of “Procreative Beneficence” stating that “couples (or single reproducers) should select the child, of the possible children they could have, who is expected to have the best life, or at least as good a life as the others, based on the relevant, available information.” (Opponents to procreative beneficence vociferously pointed out that – regrettably – Savulescu’s principle would likely lead to populations dominated by tall, pale males…).
“One child, one teacher, one book, one pen can change the world.”
These are the inspirational words of activist Malala Yousafzai, best known as “the girl who was shot by the Taliban” for championing female education in her home country of Pakistan. This modest, pared-down idea of schooling is cherished by many. There is something noble about it, perhaps because harkens back to the very roots of intellectual enquiry. No tools and no distractions; just ideas and conversation.
Traditionalists may be reminded of the largely bygone “chalk and talk” methods of teaching, rooted in the belief that students need little more than firm, directed pedagogical instruction to prepare them for the world. Many still reminisce about these relatively uncomplicated teaching techniques, but we should be careful not to misread Yousafzai’s words as prescribing simplicity as the optimal conditions for education.
On the contrary, her comments describe a baseline.
Credit: Tanisha Bassan
There is strong evidence to show that subject-specific experts frequently fall short on their informed judgments. Particularly when it comes to forecasting.
In fact, in 2005 the University of Pennsylvania’s Professor Philip E. Tetlock devised a test for seasoned and respected commentators that found as their level of expertise rose, their confidence also rose – but not their accuracy. Repeatedly, Tetlock’s experts attached high probability to low frequency events in error, relying upon intuitive casual reasoning rather than probabilistic reasoning. Their assertions were often no more reliable than, to quote the experimenter, “a dart throwing chimp.”
I was reminded of Tetlock’s ensuing book and other similar experiments at the Future Trends Forum in Madrid last month; an event that (valiantly) attempts to convene a room full of thought leaders and task them with predicting our future. Specifically, in this case, our AI future.
A “Virtual” or “Digital” Human. Credit: Digital Domain
The #AIShowBiz Summit 3.0 – which took place last month – sits apart from the often dizzying array of conferences vying for the attention of Bay Area tech natives. Omnipresent AI themes like “applications for deep learning”, “algorithmic fairness”, and “the future of work” are set aside in preference for rather more dazzling conversations on topics like “digital humans”, “AI and creativity”, and “our augmented intelligence digital future.”
It’s not that there’s anything wrong with the big reoccuring AI themes. On the contrary, they are front-and-center for very good reason. It’s that there’s something just a little beguiling about this raft of rather more spacey, futuristic conversations delivered by presenters who are unflinchingly “big picture”, while still preserving necessary practical and technical detail.
“How do we get humans to trust in all this AI we’re building?”, asked Affectiva CEO Rana El-Kaliouby, at the prestigious NYT New Work Summit at Half Moon Bay last week. She had already assumed a consensus that trust-building was the correct way to proceed, and went on to suggest that, rather than equipping users and consumers with the skills and tools to scrutinize AI, we should instead gently coax them into placing more unearned faith in data-driven artifacts.
But how would this be accomplished? Well, Affectiva are “on a mission to humanize technology”, drawing upon machine and deep learning to “understand all things human.” All things human, El-Kaliouby reliably informed us, would include our emotions, our cognitive state, our behaviors, our activities. Note: not to sense, or to tentatively detect, but to understand those things in “the way that humans can.”
Grandiose claims, indeed.
This article was originally posted on the RE•WORK blog.
The way people interact with technology is always evolving. Think about children today – give them a tablet or a smartphone and they have literally no problem in figuring out how to work it. Whilst this is a natural evolution of our relationships with new tech, as it becomes more and more ingrained in our lives it’s important to think about the ethical implications. This isn’t the first time I’ve spoken about ethics and AI – I”ve had guests on the Women in AI Podcast such as Cansu Canca from the AI Ethics Lab and Yasmin J. Erden from St Mary’s University amongst others join me to discuss this area, and I even wrote a white paper on the topic which is on RE•WORK’s digital content hub – so it’s something that’s really causing conversation at the moment. Fiona McEvoy, the founder of YouTheData.com, joined me on the podcast back in June to discuss the importance of collaboration in AI to ensure it’s ethically sound. Fiona will be joining us at the Deep Learning Summit in San Francisco this week, so in advance of this, I caught up with her to see what she’s been working on…
This article by Fiona J McEvoy (YouTheData.com) was originally posted on All Turtles.
We’re still just a few days into the New Year and all eyes have been trained on Las Vegas, NV. Over the last week or so, the great and the good of the consumer tech industry have been shamelessly touting their wares at CES. Each jockeying to make a big noise in a crowded market by showcasing “life-enhancing products” with whizzy new features—like this “intelligent toilet”…
In the organized chaos of nearly 4.5k exhibitors and a staggering 182k delegates, pundits have been working overtime to round-up the best and the rest. At the same time, commentators have been trying to distill core themes and make sage judgments about the tech trajectory of 2019.
In truth, no matter what gadgetry emerges victorious in the end of CES, there will still be some fundamental “meta themes” affecting technology in 2019. And though they may not have secured as many column inches as cutsie robots and 5G this week, these core topics are likely to have more staying power.
This article by Fiona J McEvoy (YouTheData.com) was originally posted on All Turtles.
Movie tickets bought, travel booked, customer service problems resolved. Chatbots perform so many tasks that the best ones blend into the background of everyday transactions and are often overlooked. They’re being adopted seamlessly by one industry after the next, but their next widespread application poses unique challenges.
Now healthbots are poised to become the new frontline for triage, replacing human medical professionals as the first point of contact for the sick and the injured.