A recent survey conducted by O’Reilly paints this exact picture. It gives AI-creators an in-depth look at how consumers identify and use AI technology, showcasing the heightened misunderstanding that consumers have of AI and its use.

AI takes over popular culture

Television and the big screen have played a large role in introducing AI into our homes, but how does this depiction impact how we develop and implement the technology?

For those working to incorporate AI technology into products and develop new ways to use it, robots and cars are not an everyday focus. The areas of advancement instead look at AI that learns from our actions to more efficiently help us in our day-to-day lives, answering questions for us and completing tasks through speech recognition and language processing at work and at home.

But how do we harness the excitement around the fantasy of AI to increase everyday adoption?

The true potential of AI

One of the best ways to merge the fantasy and reality of AI is to truly understand what consumers think and what they believe is the potential of the technology.

In our survey, when asked what the most useful form of AI is, more than half (58%) of consumers regarded smart home technology as the most vital. This was closely followed by home security systems (54%), travel recommendations (52%), and virtual assistants (50%). This provides insight into how AI creators can expand their ideas of where AI can be useful to encourage consumers to adopt it in their personal lives.

While AI is already present in our homes—thanks to smart speakers from Amazon, Apple and Google—more and more consumer groups appreciate the success of smart home technology and are willing to adopt it in the future.

Answering the questions: What is AI? And why should I care?

Survey respondents were also asked what application of AI excited them the most in the future. Fraud detection (28%) topped the list as the most exciting area for AI development.

It was the most commonly cited use by men. This is despite only 11% of consumers closely associating fraud detection with AI.

While self-driving cars also generated great excitement among 24% of respondents, interestingly, it was the most popular choice among women, younger consumers, and those working in the AI industry by a significant margin (50%). With fraud detection coming out on top, we can start to see the shift from fantasy to practicality, a trend that AI-creators should leverage to reinforce the pragmatic use of AI within the workforce.

It is up to a wide range of individuals including developers, marketers, product managers and sales to ensure that AI is used and understood correctly. For successful consumer AI adoption, developers should focus their efforts on leveraging AI to make consumers’ everyday lives easier, augmenting existing experiences to make them more seamless and exciting. While there might be an indifference with fantasy and reality, more and more consumer groups appreciate the success of smart home technology and are watching the development of autonomous vehicles very closely.

It’s up to these sectors to capitalise on the hype, but the results are also a call for the creators of work-focused AI to make solutions that capture the imagination and generate excitement. Not only this, but developers need to have in mind consumer needs relatively clearly even at the start of the process when an idea might be more amorphous.

What’s next?

What does the future hold for AI? While the notion of AI has been perpetuated in popular culture and science fiction throughout our lives, individuals are yet to understand the meaning that AI has and that it isn’t an ‘out-there’ concept. In fact, it is already all around us. It plays a role in our homes and at work, in ways that we wouldn’t expect.

Ultimately, AI creators need to bear this in mind and continuously learn from consumer attitudes towards AI to ensure individuals continue to stay engaged with technology, making the most out of the fantasy.

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The intersection of technology, community, and sustainability offers a plethora of opportunities to innovate. We still live in an era where millions of people are under extreme poverty, lacking access to clean water, basic sanitation, electricity, internet, quality education, and healthcare.

Clearly, we need global solutions to tackle the grandest challenges facing our planet. So how can artificial intelligence (AI) assist in addressing key humanitarian and sustainable development challenges?

To begin with, the United Nations Sustainable Development Goals (SDGs) represent a collection of 17 global goals that aim to address pressing global challenges, achieve inclusive development, and foster peace and prosperity in a sustainable manner by 2030. AI enables the building of smart systems that imitate human intelligence to solve real-world problems.

Recent advancements in AI have radically changed the way we think, live, and collaborate. Our daily lives are centred around AI-powered solutions with smart speakers playing wakeup alarms, smart watches tracking steps in our morning walk, smart refrigerators recommending breakfast recipes, smart TVs providing personalised content recommendations, and navigation mobile apps recommending the best route based on real-time traffic. Clearly, the age of AI is here. How can we leverage this transformative technology to amplify the impact for social good?

Accelerating AI-powered social innovations

AI core capabilities like machine learning (ML), computer vision, natural language understanding, and speech recognition offer new approaches to address humanitarian challenges and amplify the positive impact on underserved communities. ML enables machines to process massive amounts of data, interconnect underlying patterns, and derive meaningful insights for decision making. ML techniques like deep learning offer the powerful capability to create sophisticated AI models based on artificial neural networks.

Such models can be used for numerous real-world situations, like pandemic forecasting. AI tools can model and predict the spread of outbreaks like Covid-19 in low-resource settings using recent outbreak trends, treatment data, and travel history. This will help governmental and healthcare agencies to identify high-risk areas, manage demand and supply of essential medical supplies, and formulate localised remedial measures to control an outbreak.

Computer vision techniques process visual information in digital images and videos to generate valuable inference. Trained AI models assist medical practitioners to examine clinical images and identify hidden patterns of malignant tumors supporting expediated decision-making and a treatment plan for patients. Most recently, smart speakers have extended their conversational AI capabilities for healthcare use cases like chronic illness management, prescription ordering, and urgent-care appointments.

This advancement opens up the possibility to drive healthcare innovations that will break down access barriers and deliver quality healthcare to a marginalised population. Similarly, global educational programs aimed to connect the digitally unconnected can leverage satellite images and ML algorithms to map school locations. AI-powered learning products are increasingly launched to provide personalised experiences to train young children in math and science.

The convergence of AI with the Internet of Things (IoT) facilitates rapid development of meaningful solutions for agriculture to monitor soil health, assess crop damage, and optimise use of pesticides. This empowers local farmers to model different scenarios and choose the right crop that is likely to maximise the quality and yield, and it contributes toward zero hunger and economic empowerment SDGs.

Decoding best program practices

To deliver high social impact, AI-driven humanitarian programs should follow a “bottom-up” approach. One should always work backwards from needs of the end-user, drive clarity on the targeted community/user, their major pain points, the opportunity to innovate, and expected user experience.

Most importantly, always check whether AI is relevant to the problem at hand or investigate if a meaningful alternative approach exists. Understand how an AI-powered solution will deliver value to various stakeholders involved and positively contribute toward achieving SDG for local communities. Define a suite of metrics to measure various dimensions of program success. Data acquisition is central to building robust AI models that require access to meaningful and quality data.

Delivering effective AI solutions to the humanitarian landscape requires a clear understanding of the data required and relevant sources to acquire them. For instance, satellite images, electronic health records, census data, educational records, and public datasets are used to solve problems in education, healthcare, and climate change. Partnership with key field players is important for addressing data gaps for domains with sparsely available data.

Responsible use of AI in humanitarian programs can be achieved by enforcing standards and best practices to implement fairness, inclusiveness, security, and privacy controls. Always check models and datasets for bias and negative experiences. Techniques like data visualisation and clustering can evaluate a dataset’s distribution for fair representation of various stakeholders’ dimensions. Routine updates to training and testing datasets is essential to fairly account for diversity in users’ growing needs and usage patterns. Safeguard sensitive user information by implementing privacy controls like encrypting user data at rest and in transit, limit access to user data and critical production systems based on least-privilege access control, and enforce data retention and deletion policy on user datasets. Implement a robust threat model to handle possible system attacks and routine checks on infrastructure security vulnerabilities.

To conclude, AI-powered humanitarian programs offer a transformative opportunity to advance social innovations and build a better tomorrow for the benefit of humanity.

Photo by Elena Mozhvilo on Unsplash

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The research, titled AI Skills in the UK, shines a spotlight on some concerning issues.

For its UK report, Microsoft used data from a global AI skills study featuring more than 12,000 people in 20 countries to see how the UK is doing in comparison to the rest of the world.

Most notably, compared to the rest of the world, the UK is seeing a higher failure rate for AI projects. 29 percent of AI ventures launched by UK businesses have generated no commercial value compared to the 19 percent average elsewhere in the world.

35 percent of British business leaders foresee an AI skills gap within two years, while 28 percent believe there already is one (above the global average of 24%).

However, it seems UK businesses aren’t helping to prepare employees with the skills they need. Just 17 percent of British employees have been part of AI reskilling efforts (compared to the global figure of 38 percent.)

Agata Nowakowska, AVP EMEA at Skillsoft, said:

“UK employers will have to address the growing digital skills gap within the workforce to ensure their business is able to fully leverage every digital transformation investment that’s made. With technologies like AI and cloud becoming as commonplace as word processing or email in the workplace, firms will need to ensure employees can use such tools and aren’t apprehensive about using them.

Organisations will need to think holistically about managing reskilling, upskilling and job transitioning. As the war for talent intensifies, employee development and talent pooling will become increasingly vital to building a modern workforce that’s adaptable and flexible. Addressing and easing workplace role transitions will require new training models and approaches that include on-the-job training and opportunities that support and signpost workers to opportunities to upgrade their skills.” 

Currently, a mere 32 percent of British employees feel their workplace is doing enough to prepare them for an AI-enabled future (compared to the global average of 42%)

“The most successful organisations will be the ones that transform both technically and culturally, equipping their people with the skills and knowledge to become the best competitive asset they have,” comments Simon Lambert, Chief Learning Officer for Microsoft UK.

“Human ingenuity is what will make the difference – AI technology alone will not be enough.”

AI brain drain

It’s well-documented that the UK suffers from a “brain drain” problem. The country’s renowned universities – like Oxford and Cambridge – produce globally desirable AI talent, but they’re often swooped up by Silicon Valley giants who are willing to pay much higher salaries than many British firms.

In one example, a senior professor from Imperial College London couldn’t understand why one of her students was not turning up to any classes. Most people wouldn’t pay £9,250 per year in tuition fees and not turn up. The professor called her student to find out why he’d completed three years but wasn’t turning up for his final year. She found that he was offered a six-figure salary at Apple. 

This problem also applies to teachers who are needed to pass their knowledge onto the future generations. Many are lured away from academia to work on groundbreaking projects with almost endless resources, less administrative duties, and be paid handsomely for it too.

Some companies, Microsoft included, have taken measures to address the brain drain problem. After all, a lack of AI talent harms the entire industry.

Dr Chris Bishop, Director of Microsoft’s Research Lab in Cambridge, said:

“One thing we’ve seen over the past few years is: because there are so many opportunities for people with skills in machine learning, particularly in industry, we’ve seen a lot of outflux of top academic talent to industry.

This concerns us because it’s those top academic professors and researchers who are responsible not just for doing research, but also for nurturing the next generation of talent in this field.”

Since 2018, Microsoft has funded a program for training the next generation of data scientists and machine-learning engineers called the Microsoft Research-Cambridge University Machine Learning Initiative.

Microsoft partners with universities to ensure it doesn’t steal talent, allows employees to continue roles in teaching, funds some related PhD scholarships, sends researchers to co-supervise students in universities, and offers paid internships to work alongside teams at Microsoft on projects.

You can find the full AI Skills in the UK report here.

(Photo by William Warby on Unsplash)

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The new video courses assume the developer already has a basic standard of Python skills. If you don’t, I’m afraid you’ll need to brush up on those first. Fortunately, Microsoft released a 44-part “Python for Beginners” series last autumn (or “fall” for our American friends.)

For those with the Python skills, or if you’ve just consumed all 44-parts of Microsoft’s course in record time, the new courses are around three hours each. 

The first course, More Python for Beginners, features 20 videos and covers areas such as lambdas, inheritance, and asynchronous operations.

The second course, Even More Python for Beginners: Data Tools, consists of 31 videos and really dives into using Python for machine learning and data science. Students are taught how to use popular Python libraries for the aforementioned topics; along with using the Jupyter Notebooks browser-based development environment.

Each of the courses are still led by Christopher Harrison, senior program manager at Microsoft, and Susan Ibach, business development manager from Microsoft AI Gaming. 

“While we’re not going to get into conversations about choosing algorithms or building models, we are going to introduce what you’ll use when you begin the journey. We’ll highlight Jupyter Notebooks, the favorite tool of data scientists,” Harrison and Ibach wrote in a blog. 

As of writing, the first part in Microsoft’s Python course series has been viewed over 1.75 million times.

The course’s popularity is of little surprise given the huge interest in Python as AI talent becomes more in-demand; with six-figure salaries not unheard of. In last year’s GitHub Octoverse report, Python overtook Java to become the second most popular language on the world’s largest repository host.

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A research team at MIT developed software, called TextFooler, which looked for words which were most crucial to an NLP classifier and replaced them. The team offered an example:

“The characters, cast in impossibly contrived situations, are totally estranged from reality”, and
“The characters, cast in impossibly engineered circumstances, are fully estranged from reality”

No problem for a human to decipher. Yet the results on the AIs were startling. For instance BERT, Google’s neural net, was worse by a factor of up to seven at identifying whether reviews on Yelp were positive or negative.

Douglas Heaven, writing a roundup of the study for MIT Technology Review, explained why the research was important. “We have seen many examples of adversarial attacks, most often with image recognition systems, where tiny alterations to the input can flummox an AI and make it misclassify what it sees,” Heaven wrote. “TextFooler shows that this style of attack also breaks NLP, the AI behind virtual assistants – such as Siri, Alexa and Google Home – as well as other language classifiers like spam filters and hate-speech detectors.”

This publication has explored various methods where AI technologies are outstripping human efforts, such as detecting breast cancer, playing StarCraft, and public debating. In other fields, resistance – however futile – remains. In December it was reported that human drivers were still overall beating AIs at drone racing, although the chief technology officer of the Drone Race League predicted that 2023 would be the year where AI took over.

The end goal for software such as TextFooler, the researchers hope, is to make NLP systems more robust.

Postscript: For those reading from outside the British Isles, China, and certain Commonwealth countries – to ‘snooker’ someone, deriving from the sport of the same name, is to ‘leave one in a difficult position.’ The US equivalent is ‘behind the eight-ball’, although that would have of course thrown the headline out.

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As AI has become more sophisticated, and its ability to perform human tasks accelerates exponentially, we are seeing a moral wrangling of what this means for humanity as a whole.

The changing shape of work

Until recently, humans have developed technology to handle specific routine tasks. A human needed to break down complex processes into component tasks, determine how to automate each of those tasks, and to create and refine the automation process. AI is different because it can evaluate, select, act, and learn from its actions and be independent and self-sustaining.

The ‘death of the worker’ bell has been tolled for some time now, while a Google search will throw out reams of well-educated commentators talking on the rise of the robots. Both views are completely valid because AI is going to do far more than eliminate repetitive manual jobs. It will change any role that involves routine problem-solving within existing structures. Indeed, jobs like customer service, travel planning, medical diagnostics and even clothing design are already increasingly automated. But look past the job role and look to the wider socio economic changes access to wealth and jobs stimulates. This shows the human cost of AI and it’s not driven by robots but by the people actually creating the AI in the first instance.

Understanding the behaviour of AI

The lack of STEM skills in the world is no secret. In fact, it’s a major problem. But the perception is that this is purely a volume issue is misplaced. Of course, we need more developers, programmers and designers but actually, the background, sex and emotional intelligence of these people is equally important. This is because, now that we understand AI more, we know that the best answer isn’t necessarily the most intelligent technology, but actually one that best mimics human behaviour.

For years, the exponential growth in AI has been led by a very particular demographic of introverted, middle-aged male individuals. Namely, those that develop it. As technology has evolved, this has manifested itself in the behaviours of AI programs to a stage where they are not always reflective of the behaviours of the broader populous. Consequently, we are coming full circle where the development of AI requires a much greater spread of emotional and personality types that can create systems that interact appropriately.

Latest research from the World Economic Forum forecasts that by 2025, machines will perform more current work tasks than humans, compared to 71% being performed by humans today. However, the rapid evolution of machines and algorithms in the workplace could potentially create 133 million new roles. This is in place of the 75 million that will be displaced between now and 2022. The study also reports that while technical skills are highly sought after, they need to be supplemented with strong social and collaboration skills. Therefore a combination of social skills, including emotional intelligence, and technical skills, such as programming or equipment operation is the panacea both in the creation and operation of AI.

Passing the human test

To stay ahead of AI in an increasingly automated world, we need to start cultivating our most human abilities on a societal level too. While AI may continue to change jobs, machines won’t replace our judgment and empathy. It’s still us, the human beings, holding the power to determine who fits best in what roles and what position based on in-person interaction.

AI is brilliant at automating routine knowledge work and generating new insights from existing data. What it can’t do is deduce the existence, or even the possibility, of information it isn’t already aware of. It can’t imagine radical new products and business models or envision unimagined opportunities and achievements. AI doesn’t even have common sense.

The genetics of AI

So we desperately need the microscopic, but highly talented group of individuals that are responsible for developing AI programmes to be an accurate cross section of society. Not just in demographics – race, age, sex and location – but their genetic personality make-up. At the moment there is not enough value placed on these so-called “‘soft skills’” enough to prioritise them in the ‘build process’.

AI may be the next wave of innovation, but unique human skills such as flexibility, creativity, empathy and emotional intelligence will always be necessary to help achieve a sustainable and successful future. AI will never have our competency in emotional intelligence, cross-cultural awareness, curiosity, critical thinking, and persistence organically. Nor will they emerge on their own given enough time. Imbuing these traits in the technology is our collective responsibility. That’s how we’ll keep coming up with groundbreaking new ideas, including AI itself.

About the author: Antony Edwards is COO at Eggplant.io. Antony is a proven product and technology leader with extensive experience in enterprise software and mobile computing. Prior to Eggplant, Antony served as CTO of The Global Draw Group—a leading supplier of networked gaming products, which includes subsidiaries Barcrest and Games Media. Antony also served as a member of the Executive Team and EVP of Ecosystem and Technical Services at mobile operating system specialist Symbian.

Attend the co-located AI & Big Data Expo events with upcoming shows in Silicon Valley, London, and Amsterdam to learn more. Co-located with the IoT Tech Expo, , and Cyber Security & .

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Many examples of this, as with many industries disrupted by AI, have explored how artificial intelligence will augment teachers’ current skills, rather than entirely eradicating it. Speaking to McKinsey this time last year, Jennifer Rexford, computer science chair at Princeton University, noted that if you flip the question – what is AI not good at? – then social skills and creativity are the key remaining issues.

“Machines may behave in ways that seem creative, but they’re really born of exhaustive enumeration and evaluation of the underlying data,” said Rexford. “It’s not born of that spark of creativity.

“That’s a clarion call for thinking about not only retraining but even basic education,” Rexford added. “The way we teach today… doesn’t put enough emphasis on creativity and social perceptiveness and design and working in teams.”

Yet not every education system is the same. Take China for instance. It is the largest education system in the world – but according to one company, it is not entirely equal. “There is high quality education, however high-quality teachers are rare,” explains Dr Wei Cui, partner at Squirrel AI Learning. “One in a million. It’s [also] very expensive to hire high quality teachers – especially for a personalised education plan.”

Squirrel AI aims to provide ‘adaptive learning’ for students across all regions and cities in China by utilising artificial intelligence and big data analysis techniques. As every student has his or her own individual capacity for learning, the company argues, traditional, linear teaching methods, are not as effective. Hence a personalised plan, which assesses a student’s point of learning, then focuses almost exclusively on their weaknesses.

The company’s award-winning solution – it picked up the Bloomberg Businessweek Business Innovation Award of 2018 earlier this month – is now used across approximately 300 cities in China, with 200 learning centres. ‘Let each child have an AI super teacher’ is the company’s tag line.

“Every student can learn at their own pace,” explains Wei. “The students can have their own customised learning materials and learning path. Our system is to improve students’ learning efficiency.

“For example, [if] every student needs to spend two hours on their homework, using our system the high performance students only need to take about 30 minutes – that’s it.”

The egalitarian nature of the product focuses on bridging the gap for education in China by both cost and gender. Many times, it is both at once. Dr Wei argued families with lower incomes, if they can only choose one child – China’s one-child policy having been relaxed in 2013 – will choose boys over girls.

Writing for Forbes in December, Joe McKendrick noted how the system does not mitigate between a ‘good’ or ‘poor’ student. “With an AI engine supporting personalised instruction, coursework can focus on strengthening their understanding in key subjects,” he wrote.

“We can help every family for high quality education,” Wei added. “The students – their family doesn’t need to pay to go to the high quality school, don’t need to pay for the one-to-one tutoring, the experienced teachers. The system we can replicate is anywhere, any time – it is unlimited.”

You can find out more about Squirrel AI by visiting here.

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Last week, our sister publication Developer reported AI skills are in such high demand that companies are willing to offer six figure salaries. Universities are unable to match these benefits and it’s creating a brain drain preventing the current lack of AI talent problem from being addressed.

The Guardian reports of a case from Imperial College London where a senior professor couldn’t understand why one of her students was not turning up to any classes. Most people wouldn’t pay £9,250 per year in tuition fees only to not turn up.

Eventually, the professor called her student to find out why he’d completed three years but wasn’t turning up for his final year. She found he was offered a six-figure salary at Apple.

Cases of private companies stealing academia talent is being reported up-and-down the country. With the AI skill shortage being a global issue, there’s a good chance this brain drain situation is not exclusive to the UK.

Plugging the AI brain drain

One potential solution is one backed by many citizens and employed with success in some Nordic countries, and that’s of introducing a pay cap. Those who put in the hours to learn advanced skills deserve to be paid more, but the difference between workers can often be grotesque.

In fact, according to an analysis published by the Equality Trust, the average FTSE chief executive earns 386 times more than a worker on the national living wage.

Capping salaries would also help to make the market more competitive by helping startups who are unable to match the buying power of the likes of Google, Amazon, Facebook, and Apple. These companies represent some of the handful of firms where talent is being disproportionately concentrated.

Tackling the tax avoidance of many large companies must also be a priority to ensure governments have the money to reinvest in education.

Of course, too much regulation can be counterproductive. Talent may flock to countries where there is no pay cap, and companies may decide to set up their businesses and the jobs they provide in other nations with less strict tax policies.

Some teachers are admirably turning down job offers from big companies despite the large salaries, greater resources, often less administrative duties, the ability to work alongside some of the best in the business, and on potentially groundbreaking projects. They do this just to ensure future generations can learn important skills.

These people are frankly heroes — there are not many of us who could turn down such attractive propositions. We salute you.

How should the brain drain in AI be tackled?

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