Dr Alex Young is a trained orthopaedic and trauma surgeon who founded Virti and set out to use emerging technologies to provide immersive training for both new healthcare professionals and experienced ones looking to hone their skills.

COVID-19 has put most in-person training on hold to minimise transmission risks. Hospitals and universities across the UK and US are now using the virtual patient as a replacement—including our fantastic local medics and surgeons at the Bristol NHS Foundation Trust.

The virtual patient uses Natural Language Processing (NLP) and ‘narrative branching’ to allow medics to roleplay lifelike clinical scenarios. Medics and trainees can interact with the virtual patient using their tablet, desktop, or even VR/AR headsets for a more immersive experience.

Dr Alex Young comments:

“We’ve been working with healthcare organisations for several years, but the pandemic has created really specific challenges that technology is helping to solve. It’s no longer safe or practicable to have 30 medics in a room with an actor, honing their clinical soft-skills. With our virtual patient technology, we’ve created an extremely realistic and repeatable experience that can provide feedback in real time. This means clinicians and students can continue to learn valuable skills.

Right now, communication with patients can be very difficult. There’s a lot of PPE involved and patients are often on their own. Having healthcare staff who are skilled in handling these situations can therefore make a huge difference to that patient’s experience.”

Some of the supported scenarios include: breaking bad news, comforting a patient in distress, and communicating effectively whilst their faces are obscured by PPE. Virti’s technology was also used at the peak of the pandemic to train NHS staff in key skills required on the front line, such as how to safely use PPE, how to navigate an unfamiliar intensive care ward, how to engage with patients and their families, and how to use a ventilator.

Tom Woollard, West Suffolk Hospital Clinical Skills and Simulation Tutor, who used the Virti platform at the peak of the COVID pandemic, comments:

“We’ve been using Virti’s technology in our intensive care unit to help train staff who have been drafted in to deal with COVID-19 demand.

The videos which we have created and uploaded are being accessed on the Virti platform by nursing staff, physiotherapists and Operational Department Practitioners (ODPs) to orient them in the new environment and reduce their anxiety.

The tech has helped us to reach a large audience and deliver formerly labour-intensive training and teaching which is now impossible with social distancing.

In the future, West Suffolk will consider applying Virti tech to other areas of hospital practice.”

The use of speech recognition, NLP, and ‘narrative branching’ provides a realistic simulation of how a patient would likely respond—providing lifelike responses in speech, body language, and mannerisms.

The AI delivers real-time feedback to the user so they can learn and improve. With upwards of 70 percent of complaints against health professionals and care providers attributable to poor communication, the virtual patient could help to deliver better care while reducing time spent handling complaints.

Virti’s groundbreaking technology has – quite rightly – been named one of TIME’s best inventions of 2020.

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Duplex, for a quick primer, is Google’s AI-powered voice bot which can call businesses on a person’s behalf for things such as booking hair appointments. It’s so realistic that everyone has decided that bots must declare themselves as such before chatting with a human.

A company known as PolyAI – which specialises in “enterprise-ready voice assistants” – has posted an account of what happened when Duplex called one of its restaurant assistants.

Duplex was calling businesses over the summer to update opening hours on Google Maps. This is how the conversation went:

Nikola Mrkšić, Co-Founder and CEO of PolyAI, wrote in a blog post:

“As far as we’re aware, this is the first naturally-occurring conversation between AI voice assistants in the wild.

I have never seen anything like this before, and I’m incredibly proud that PolyAI is sharing this moment in computing history with our friends from Google.”

Mrkšić humbly admits that Duplex sounds far more human-like than PolyAI’s assistant. However, he also makes a valid reference to the “uncanny valley” theory.

The uncanny valley theory suggests that people are more positive towards something which sounds like a human, up until a point. When it sounds too much like a human then it becomes creepy—a sentiment which many have certainly shared about Duplex.

(Photo by Jeffery Ho on Unsplash)

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Duplex probably needs little introduction as it caused a bit of a stir when it debuted at I/O in late 2018 (when conferences were things you could still physically attend.)

The human-sounding AI could perform actions like calling a business on a person’s behalf and booking in things such as hair appointments or table reservations.

Duplex is undeniably impressive, but it prompted a debate over whether AIs should have to state they’re not human before imitating one. Google has since decided to add disclosures at the beginning of calls and give businesses the option to opt-out of being called by an AI.

Humans haven’t been completely replaced by Duplex. Google says around a quarter of Duplex calls are started by humans, and 15 percent start with an AI but are later intervened by a human if issues arise or the person receiving the call opts not to speak with an AI.

In terms of devices, the rollout of Duplex started on Pixel phones (obviously) before making the slightly odd decision to launch on iOS devices. More Android phones then began joining the party.

(Photo by Quino Al on Unsplash)

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Voysis specialises in improving digital assistants inside online shopping applications wherein the software could respond more precisely to voice commands. According to the start-up, its technology could narrow product search results by processing shopping phrases like “I need a new LED TV” and “My budget is $1,000.” This AI was provided to other companies for use in their own applications and voice assistants.

The acquisition of Voysis would give Siri an edge to perform better than the Google Assistant, which many in the industry say has a notable lead in natural language comprehension and processing.

Voysis uses an AI-based method called WaveNets to create more human-like computer speech. In 2018, co-founder Peter Cahill had said that his company managed to shrink its system to the point where, once the AI is trained, the software uses as little as 25MB of memory, which makes it easier to run on smartphones without an internet connection.

This is not the first instance where Apple has made a big bet in this space. In January, Apple acquired Seattle-based edge AI specialist Xnor.ai for approximately $200m. Xnor.ai is the same company that once powered the person-detection feature on Wyze’s popular cameras. The Cupertino-based tech giant has been on something of an AI acquisition spree in recent years. A report by CBInsights found that Apple acquired more AI firms (20) than any other leading tech company in 2019.

This time last year, Apple acquired AI company Laserlike to add to its growing roster of in-house talent. Laserlike is known for its AI-powered app which makes it easier for users to follow news topics. Most notably, it was founded by former Google engineers.

Photo by Medhat Dawoud on Unsplash

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Governments around the world are racing to obtain sufficient and effective testing kits to diagnose COVID-19. The current widely-used test requires a thin cotton swab to be put up the nasal cavity and reach to the back of the throat. It’s not a painful procedure, but it’s invasive and uncomfortable.

COVID-19 tests which only require a finger prick are starting to be rolled out but obtaining sufficient numbers of any test is proving difficult. On Tuesday, British cabinet minister Michael Gove said the UK was being hindered by the global shortage of chemical reagents needed for testing.

If the researchers from Carnegie Mellon are successful, a test that could be taken at home instantly could be rolled out. While it’s unlikely to ever be as accurate as a full test, it could help to prioritise where limited resources should be allocated and determine which households are more likely to be suffering from seasonal flu.

Speaking to Futurism, Benjamin Striner, a graduate working on the project, said: “I’ve seen a lot of competition for the cheapest, fastest diagnosis you can have.”

“And there are some pretty good ones that are actually really cheap and pretty accurate, but nothing’s ever going to be as cheap and as easy as speaking into a phone.”

Coronavirus is a respiratory illness and therefore affects breathing patterns and other vital parameters. The AI system analyses a person’s voice and provides a score on the likelihood that the individual has coronavirus based on markers observed from known sufferers.

The researchers are currently asking both healthy and infected people to share a recording of their voice to help improve the algorithm.

Yours truly has already submitted his voice. The process takes around five minutes and requires the following seven steps:

• Submit basic demographic information.
• Cough three times.
• Say ‘a’ for as long as you can.
• Say ‘o’ for as long as you can.
• Say ‘e’ for as long as you can.
• Count to 20.
• Say the alphabet.

Be aware the app is still in its early stages and is not yet approved by agencies like the FDA or CDC. The app should also not be used as a substitute for a proper medical test or examination if you’re concerned you may have COVID-19.

If you’re interested, you can access the COVID Voice Detector here.

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Many of us know how voice services often struggle with even “normal” conversations, despite vast training datasets available. National and regional accents are difficult enough without adding in industry-specific jargon.

However, correctly understanding medical terms could mean life or death. With healthcare set to be one of the industries most revolutionised by AI, understanding the jargon is vital for it to reach its full potential safely.

Several tech giants have existing healthcare partnerships and can use their influence to obtain data in ways that are difficult for smaller competitors. Of course, many of these corporations are known to harvest personal data for their own means.

The researchers, from the Australian Institute of Health Innovation (AIHI) at Macquarie University, are aiming to build an alternative to the AI medical scribing systems being developed by tech giants.

“We need recordings of tens of thousands of conversations between patients and doctors so the AI can learn patterns from the data,” Dr Juan Quiroz, one of the researchers working on the project, told Macquarie’s Lighthouse publication.

“Doctors and clinicians rarely record their consultations,” he added.

Medical practitioners are often overburdened enough, so asking them to record consultations is both invasive and adding more to their daily work. Large financial incentives may help, which is again where the big tech giants hold a significant advantage in addition to their established partnerships.

The majority of the world’s biggest tech companies have some form of AI healthcare initiative. Just last week, AI News reported on Amazon’s announcement of its own Transcribe Medical solution.

Amazon says Transcribe Medical can be deployed across “thousands” of healthcare facilities to provide clinicians with secure note-taking abilities. The solution is covered under AWS’ HIPAA eligibility and business associate addendum (BAA) which means that any customer that enters into a BAA with AWS can process and store personal health information legally.

The researchers have an uphill battle ahead of them against players like Amazon, but it’s hard not to admire their ambition and noble cause.

You can find the AIHI researchers’ full paper published in science journal Nature here.

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The consortium includes Google DeepMind, Facebook AI, New York University, and the University of Washington. Each of the consortium’s members believe a more comprehensive benchmark is needed for NLP than current solutions.

The result is a benchmarking platform called SuperGLUE which replaces an older platform called GLUE with a “much harder benchmark with comprehensive human baselines,” according to Facebook AI. 

SuperGLUE helps to put NLP abilities to the test where previous benchmarks were beginning to pose too simple for the latest systems.

“Within one year of release, several NLP models have already surpassed human baseline performance on the GLUE benchmark. Current models have advanced a surprisingly effective recipe that combines language model pretraining on huge text data sets with simple multitask and transfer learning techniques,” Facebook said.

In 2018, Google released BERT (Bidirectional Encoder Representations from Transformers) which Facebook calls one of the biggest breakthroughs in NLP. Facebook took Google’s open-source work and identified changes to improve its effectiveness which led to RoBERTa (Robustly Optimized BERT Pretraining Approach).

RoBERTa basically “smashed it,” as the kids would say, in commonly-used benchmarks:

“Within one year of release, several NLP models (including RoBERTa) have already surpassed human baseline performance on the GLUE benchmark. Current models have advanced a surprisingly effective recipe that combines language model pretraining on huge text data sets with simple multitask and transfer learning techniques,” Facebook explains.

For the SuperGLUE benchmark, the consortium decided on tasks which meet four criteria:

1. Have varied formats.
2. Use more nuanced questions.
3. Are yet-to-be-solved using state-of-the-art methods.
4. Can be easily solved by people.

The new benchmark includes eight diverse and challenging tasks, including a Choice of Plausible Alternatives (COPA) causal reasoning task. The aforementioned task provides the system with the premise of a sentence and it must determine either the cause or effect of the premise from two possible choices. Humans have managed to achieve 100 percent accuracy on COPA while BERT achieves just 74 percent.

Across SuperGLUE’s tasks, RoBERTa is currently the leading NLP system and isn’t far behind the human baseline:

You can find a full breakdown of SuperGLUE and its various benchmarking tasks in a Facebook AI blog post here.

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Degenerative diseases like amyotrophic lateral sclerosis (ALS) are known for causing speech impairments. Today’s voice recognition systems often cannot recognise the speech of individuals suffering from such diseases, despite those individuals arguably set to benefit the most from the automation offered by the technology.

Google has set out to solve the problem with Project Euphonia.

Dimitri Kanevsky, a Google researcher who himself has impaired speech, can be seen in the video below using a system called Parrotron to convert his speech into one understandable by Google Assistant:

The researchers provide a background of Project Euphonia’s origins:

“ASR [automatic speech recognition] systems are most often trained from ‘typical’ speech, which means that underrepresented groups, such as those with speech impairments or heavy accents, don’t experience the same degree of utility.

…Current state-of-the-art ASR models can yield high word error rates (WER) for speakers with only a moderate speech impairment from ALS, effectively barring access to ASR reliant technologies.”

As the researchers highlight, part of the problem is that training sets primarily consist of ‘typical speech’ without much-needed variety to represent all parts of society (this even includes heavy accents, to some degree.)

The researchers set out to record dozens of hours of voice recordings from individuals with ALS to help train their AI. However, the resulting training set is still not ideal as each person with ALS sounds unique dependent on the progression of the disease and how it’s affecting them.

Google was able to reduce its word error rate by using a baseline voice recognition model, experimenting with some tweaks, and training it with the new recordings.

The method substantially improved the recognition but the researchers found it could occasionally struggle with phonemes in one of two key ways:

1. The phoneme isn’t recognised and therefore the word along with it,
2. The model has to guess at what phoneme the speaker meant.

The second problem is fairly trivial to solve. By analysing the rest of the sentence’s context, the AI can often determine the correct phoneme. For example, if the AI hears “I’m reading off to the cub,” it can probably determine the user meant “I’m heading off to the pub”.

You can read the full paper on arXiv here ahead of its presentation at the Interspeech conference in Austria next month.

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Current digital assistants listen for a wakeword such as “Ok, Google” or “Alexa,” before recording speech for processing. Especially for companies such as Google and Amazon which thrive on knowing everything about users, this helps to quell privacy concerns.

There are some drawbacks from this approach, mainly context. Future AI assistants will be able to provide more help when armed with information leading up to the request.

For example, say you were discussing booking a seat at your favourite restaurant next Tuesday. After asking, “Alexa, do I have anything on my schedule next Tuesday?” it could respond: “No, would you like me to book a seat at the restaurant you were discussing and add it to your calendar?”

Today, such a task would require three separate requests.

Amazon’s patent isn’t quite as complex just yet. The example provided in the filing envisions allowing the user to say things such as “Play ‘And Your Bird Can Sing’ Alexa, by the Beatles,” (Note the wakeword after the play song command.)

David Emm, Principal Security Researcher at Kaspersky Lab, said:

“Many Amazon Alexa users will likely be alarmed by today’s news that the company’s latest patent would allow the devices – commonplace in homes across the UK – to record everything a person says before even being given a command. Whilst the patent doesn’t suggest it will be installed in future Alexa-enabled devices, this still signals an alarming development in the further surrender of our personal privacy.

Given the amount of sensitive information exchanged in the comfort of people’s homes, Amazon would be able to access a huge volume of personal information – information that would be of great value to cybercriminals and threat actors. If the data isn’t secured effectively, a successful breach of Amazon’s systems could have a severe knock-on effect on the data security and privacy of huge numbers of people.

If this patent comes into effect, consumers need to be made very aware of the ramifications of this – and to be fully briefed on what data is being collected, how it is being used, and how they can opt out of this collection. Amazon may argue that analysing stored data will make their devices smarter for Alexa owners – but in today’s digital era, such information could be used nefariously, even by trusted parties. For instance, as we saw with Cambridge Analytica, public sector bodies could target election campaigns at those discussing politics.

There’s a world of difference between temporary local storage of sentences, to determine if the command word has been used, and bulk retention of data for long periods, or permanently – even if the listening process is legitimate and consumers have opted in. There have already been criticisms of Amazon for not making it clear what is being recorded and stored – and we are concerned that this latest development shows the company moving in the wrong direction – away from data visibility, privacy, and consent.”

There’s a joke about Uber that society used to tell you not to get into cars with strangers, and now you’re encouraged to order one from your phone. Lyft has been able to ride in Uber’s wake relatively negative PR free.

Getting the balance right between innovation and safety can be a difficult task. Pioneers often do things first and face the backlash before it actually becomes somewhat normal. That’s not advocating Amazon’s possible approach, but we’ve got to be careful outrage doesn’t halt progress while remaining vigilant of actual dangers.

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The new API is developed by using an existing deep-learning framework with expert data collection and labelling methodologies. When all these are combined with its existing emotion recognition technology for analysing facial expressions, it allows a person’s emotions to be measured across both face and speech.

Dr. Rana el Kaliouby, co-founder and CEO, Affectiva, said: “More often than not, humans’ interactions with technology are transactional and rigid. Conversational interfaces like chatbots, social robots or virtual assistants could be so much more effective if they were able to sense a user’s frustration or confusion and then alter how they interact with that person. By learning to distinguish emotions in facial expressions, and now speech, technology will become more relatable, and eventually, more human.”

A similar AI-based Chinese education company, Liulishuo, has developed an automatic assessment engine for spoken and written English. The company has a vast data collection of Chinese people speaking English. It has also recently raised $100 million in a series C funding round in order to develop the existing smart teaching platform.

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