FRA’s report is titled Getting The Future Right and opens with some of the ways AI is already making lives better—such as helping with cancer diagnosis, and even predicting where burglaries are likely to take place.

“The possibilities seem endless,” writes Michael O’Flaherty, Director of the FRA, in the report’s foreword. “But how can we fully uphold fundamental rights standards when using AI?”

The FRA interviewed over a hundred public administration officials, private company staff, and a diverse range of experts, in a bid to answer that question.

With evidence of algorithms having biases which could lead to automating societal issues like racial profiling—it’s a question that needs answering if the full potential of AI is going to be unlocked for the whole of society.

O’Flaherty says:

“AI is not infallible, it is made by people – and humans can make mistakes. That is why people need to be aware when AI is used, how it works and how to challenge automated decisions. The EU needs to clarify how existing rules apply to AI. And organisations need to assess how their technologies can interfere with people’s rights both in the development and use of AI.

“We have an opportunity to shape AI that not only respects our human and fundamental rights but that also protects and promotes them.”

AI is being used in almost every industry in some form or another—if not already, it will be soon.

Biases in AI are more dangerous in some industries than others. Policing is an obvious example, but in areas like financial services it could mean one person being given a loan or mortgage compared to another.

Without due transparency, these biases could happen without anyone knowing the reasons behind such decisions—it could simply be because someone grew up in a different neighbourhood. Each automated decision has a very real human impact.

The FRA calls for the EU to:

• Make sure that AI respects ALL fundamental rights – AI can affect many rights – not just privacy or data protection. It can also discriminate or impede justice. Any future AI legislation has to consider this and create effective safeguards.
• Guarantee that people can challenge decisions taken by AI – people need to know when AI is used and how it is used, as well as how and where to complain. Organisations using AI need to be able to explain how their systems take decisions.
• Assess AI before and during its use to reduce negative impacts – private and public organisations should carry out assessments of how AI could harm fundamental rights.
• Provide more guidance on data protection rules – the EU should further clarify how data protection rules apply to AI. More clarity is also needed on the implications of automated decision-making and the right to human review when AI is used.
• Assess whether AI discriminates – awareness about the potential for AI to discriminate, and the impact of this, is relatively low. This calls for more research funding to look into the potentially discriminatory effects of AI so Europe can guard against it.
• Create an effective oversight system – the EU should invest in a more ‘joined-up’ system to hold businesses and public administrations accountable when using AI. Authorities need to ensure that oversight bodies have adequate resources and skills to do the job.

The EU has increased its scrutiny of “big tech” companies like Google in recent years over concerns of invasive privacy practices and abusing their market positions. Last week, AI News reported that Google had controversially fired leading AI ethics researcher Timnit Gebru after she criticised her employer in an email.

Google chief executive Sundar Pichai wrote in a memo: “We need to accept responsibility for the fact that a prominent black, female leader with immense talent left Google unhappily.

“It’s incredibly important to me that our black, women, and under-represented Googlers know that we value you and you do belong at Google.”

Gebru gave an interview to the BBC this week in which she called Google and big tech “institutionally racist”. With that in mind, the calls made in the FRA’s report seem especially important to heed.

You can download a full copy of the FRA’s report here.

(Photo by Guillaume Périgois on Unsplash)

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Algorithmia’s 2021 Enterprise Trends in Machine Learning report features the views of 403 business leaders involved with machine learning initiatives.

Diego Oppenheimer, CEO of Algorithmia, says:

“COVID-19 has caused rapid change which has challenged our assumptions in many areas. In this rapidly changing environment, organisations are rethinking their investments and seeing the importance of AI/ML to drive revenue and efficiency during uncertain times.

Before the pandemic, the top concern for organisations pursuing AI/ML initiatives was a lack of skilled in-house talent. Today, organisations are worrying more about how to get ML models into production faster and how to ensure their performance over time.

While we don’t want to marginalise these issues, I am encouraged by the fact that the type of challenges have more to do with how to maximise the value of AI/ML investments as opposed to whether or not a company can pursue them at all.”

The main takeaway is that AI budgets are significantly increasing. 83 percent of respondents said they’ve increased their budgets compared to last year.

Despite a difficult year for many companies, business leaders are not being put off of AI investments—in fact, they’re doubling-down.

In Algorithmia’s summer survey, 50 percent of respondents said they plan to spend more on AI this year. Around one in five even said they “plan to spend a lot more.”

76 percent of businesses report they are now prioritising AI/ML over other IT initiatives. 64 percent say the priority of AI/ML has increased relative to other IT initiatives over the last 12 months.

With unemployment figures around the world at their highest for several years – even decades in some cases – it’s at least heartening to hear that 76 percent of respondents said they’ve not reduced the size of their AI/ML teams. 27 percent even report an increase.

43 percent say their AI/ML initiatives “matter way more than we thought” and close to one in four believe their AI/ML initiatives should have been their top priority sooner. Process automation and improving customer experiences are the two main areas for AI investments.

While it’s been all good news so far, there are AI deployment issues being faced by many companies which are yet to be addressed.

Governance is, by far, the biggest AI challenge being faced by companies. 56 percent of the businesses ranked governance, security, and auditability issues as a concern.

Regulatory compliance is vital but can be confusing, especially with different regulations between not just countries but even states. 67 percent of the organisations report having to comply with multiple regulations for their AI/ML deployments.

The next major challenge after governance is with basic deployment and organisational challenges. 

Basic integration issues were ranked by 49 percent of businesses as a problem. Furthermore, more job roles are being involved with AI deployment strategies than ever before—it’s no longer seen as just the domain of data scientists.

However, there’s perhaps some light at the end of the tunnel. Organisations are reporting improved outcomes when using dedicated, third-party MLOps solutions.

While keeping in mind Algorithmia is a third-party MLOps solution, the report claims organisations using such a platform spend an average of around 21 percent less on infrastructure costs. Furthermore, it also helps to free up their data scientists—who spend less time on model deployment.

You can find a full copy of Algorithmia’s report here (requires signup)

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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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Data from Dealroom was used for the State of European Tech report. Dealroom defines deep tech as 16 fields: Artificial Intelligence, Machine Learning, Big Data, Augmented Reality, Virtual Reality, Drones, Autonomous Driving, Blockchain, Nanotech, Robotics, Internet of Things, 3D Technology, Computer Vision, Connected Devices, Sensors Technology, and Recognition Technology (NLP, image, video, text, speech recognition).

In 2019, there was $10.2 billion capital invested in European deep tech. In 2020, that dropped to $8.9 billion:

I think it’s fair to say that 2020 has been a tough year for most people and businesses. Economic uncertainty – not just from COVID-19 but also trade wars, Brexit, and a rather tumultuous US presidential election – has naturally led to fewer investments and people tightening their wallets.

For just one example, innovative satellite firm OneWeb was forced to declare bankruptcy earlier this year after crucial funding it was close to securing was pulled during the peak of the pandemic. Fortunately, OneWeb was saved following an acquisition by the UK government and Bharti Global—but not all companies have been so fortunate.

Many European businesses will now be watching the close-to-collapse Brexit talks with hope that a deal can yet be salvaged to limit the shock to supply lines, prevent disruption to Europe’s leading financial hub, and help to build a friendly relationship going forward with a continued exchange of ideas and talent rather than years of bitterness and resentment.

The report shows the UK has retained its significant lead in European tech investment and startups this year:

Despite the uncertainties, the UK looks unlikely to lose its position as the hub of European technology anytime soon.

Investments in European tech as a whole should bounce back – along with the rest of the world – in 2021, with promising COVID-19 vaccines rolling out and hopefully some calm in geopolitics.

94 percent of survey respondents for the report stated they have either increased or maintained their appetite to invest in the European venture asset class. Furthermore, a record number of US institutions have participated in more than one investment round in Europe this year—up 36% since 2016.

You can find a full copy of the State of European Tech report here.

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The company demonstrated its latest AI model using a small dataset – just a fraction of the size typically used for a Generative Adversarial Network (GAN) – of artwork from the Metropolitan Museum of Art.

From the dataset, NVIDIA’s AI was able to create new images which replicate the style of the original artist’s work. These images can then be used to help train further AI models.

The AI achieved this impressive feat by applying a breakthrough neural network training technique similar to the popular NVIDIA StyleGAN2 model. 

The technique is called Adaptive Discriminator Augmentation (ADA) and NVIDIA claims that it reduces the number of training images required by 10-20x while still getting great results.

David Luebke, VP of Graphics Research at NVIDIA, said:

“These results mean people can use GANs to tackle problems where vast quantities of data are too time-consuming or difficult to obtain.

I can’t wait to see what artists, medical experts and researchers use it for.”

Healthcare is a particularly exciting field where NVIDIA’s research could be applied. For example, it could help to create cancer histology images to train other AI models.

The breakthrough will help with the issues around most current datasets.

Large datasets are often required for AI training but aren’t always available. On the other hand, large datasets are difficult to ensure their content is suitable and does not unintentionally lead to algorithmic bias.

Earlier this year, MIT was forced to remove a large dataset called 80 Million Tiny Images. The dataset is popular for training AIs but was found to contain images labelled with racist, misogynistic, and other unacceptable terms.

A statement on MIT’s website claims it was unaware of the offensive labels and they were “a consequence of the automated data collection procedure that relied on nouns from WordNet.”

The statement goes on to explain the 80 million images contained in the dataset – with sizes of just 32×32 pixels – meant that manual inspection would be almost impossible and couldn’t guarantee all offensive images would be removed.

By starting with a small dataset that can be feasibly checked manually, a technique like NVIDIA’s ADA could be used to create new images which emulate the originals and can scale up to the required size for training AI models.

In a blog post, NVIDIA wrote:

“It typically takes 50,000 to 100,000 training images to train a high-quality GAN. But in many cases, researchers simply don’t have tens or hundreds of thousands of sample images at their disposal.

With just a couple thousand images for training, many GANs would falter at producing realistic results. This problem, called overfitting, occurs when the discriminator simply memorizes the training images and fails to provide useful feedback to the generator.”

You can find NVIDIA’s full research paper here (PDF). The paper is being presented at this year’s NeurIPS conference as one of a record 28 NVIDIA Research papers accepted to the prestigious conference.

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Everyone knows how important adequate sleep is for recovery. However, patients in pain – or just insomniacs like me – can struggle to get the sleep they need.

“Rest is a critical element to a patient’s care, and it has been well-documented that disrupted sleep is a common complaint that could delay discharge and recovery,” said Theodoros Zanos, Assistant Professor at Feinstein Institutes’ Institute of Bioelectronic Medicine.

When a patient finally gets some shut-eye, the last thing they want is to be woken up to have their vitals checked—but such measurements are, well, vital.

In a paper published in Nature Partner Journals, the researchers detailed how they developed a deep-learning predictive tool which predicts a patient’s stability overnight. This prevents multiple unnecessary checks being carried out.

Vital sign measurements from 2.13 million patient visits at Northwell Health hospitals in New York between 2012 and 2019 were used to train the AI. Data included heart rate, systolic blood pressure, body temperature, respiratory rate, and age. A total of 24.3 million vital signs were used.

When tested, the AI misdiagnosed just two of 10,000 patients in overnight stays. The researchers noted how nurses on their usual rounds would be able to account for the two misdiagnosed cases.

According to the paper, around 20-35 percent of a nurse’s time is spent keeping records of patients’ vitals. Around 10 percent of their time is spent collecting vitals. On average, a nurse currently has to collect a patient’s vitals every four to five hours.

With that in mind, it’s little wonder medical staff feel so overburdened and stressed. These people want to provide the best care they can but only have two hands. Using AI to free up more time for their heroic duties while simultaneously improving patient care can only be a good thing.

The AI tool is being rolled out across several of Northwell Health’s hospitals.

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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 figure, which comes from the analyst firm’s latest Worldwide Artificial Intelligence Spending Guide, calculates a CAGR of 20.1% as adopting AI becomes a ‘must’ in the enterprise.

In particular, companies will utilise AI to deliver a better customer experience, as well as help employees to become better at their jobs. Automated customer service agents, sales process recommendation and automation, as well as automated threat intelligence and prevention, are the primary use cases outlined by IDC.

Retail and banking are the two industries most likely to splurge in the coming years. The former, unsurprisingly, will focus more on customer experience, while the latter will invest on fraud analysis and investigation, as well as program advisors and recommendation systems.

Other industries have hit something of a proverbial wall, primarily as a result of Covid-19. Transportation, as well as the services industry – including leisure and hospitality – have already struggled with the pandemic. Naturally, IDC argued, AI investments will be on the back burner here in 2020. Yet the pandemic has seen some innovation; the research specifically noted hospitals who were using AI to speed up Covid-19 diagnosis and testing.

“Companies will adopt AI – not just because they can, but because they must,” said Ritu Jyoti, program vice president for artificial intelligence at IDC. “AI is the technology that will help businesses to be agile, innovate, and scale. The companies that become ‘AI powered’ will have the ability to synthesise information, the capacity to learn, and the capability to deliver insights at scale.”

In other words, leading organisations will be able to use AI to convert data into information and insights, understand those relationships and apply those insights to business problems, and then support decisions and bring through automation.

Sounds simple when it’s put like that.

Photo by Fabian Blank on Unsplash

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Countries around the world are vastly increasing their budgets for AI, and with good reason. Just look at Gartner’s Hype Cycle released yesterday to see how important the technology is expected to be over the next decade.

Russian president Vladimir Putin famously said back in 2017 that the nation which leads in AI “will become the ruler of the world”. Putin said that AI offers unprecedented power, including military power, to any government that leads in the field.

China, the third global superpower, has also embarked on a major national AI strategy. In July 2017, The State Council of China released the “New Generation Artificial Intelligence Development Plan” to build a domestic AI industry worth around $150 billion over the next few years and to become the leading AI power by 2030.

Naturally, the US isn’t going to give that top podium spot to China without a fight.

The White House has proposed (PDF) a 30 percent hike in spending on AI and quantum computing. Around $1.5 billion would be allocated to AI funding and $699 million to quantum technology.

According to a report published by US national security think tank Center for a New American Security (CNAS), Chinese officials see an AI ‘arms race’ as a threat to global peace.

The fear of the CNAS is that integrating AI into military resources and communications may breach current international norms and lead to conflict-by-accident.

China and the US have been vying to become the top destination for AI investments. Figures published by ABI Research at the end of last year suggested that the US reclaimed the top spot for AI investments back from China, which overtook the Americans the year prior. ABI expects the US to reach a 70 percent share of global AI investments.

Lian Jye Su, Principal Analyst at ABI Research, said: 

“The United States is reaping the rewards from its diversified AI investment strategy. 

Top AI startups in the United States come from various sectors, including self-driving cars, industrial manufacturing, robotics process automation, data analytics, and cybersecurity.”

The UK, unable to match the levels of funding allocated to AI research as the likes of the US and China, is taking a different approach.

An index compiled by Oxford Insights last year ranked the UK number one for AI readiness in Europe and only second on the world stage behind Singapore. The US is in fourth place, while China only just makes the top 20.

The UK has focused on AI policy and harnessing the talent from its world-leading universities to ensure the country is ready to embrace the technology’s opportunities.

A dedicated AI council in the UK features:

• Ocado’s Chief Technology Officer, Paul Clarke
• Dame Patricia Hodgson, Board Member of the Centre for Data Ethics and Innovation 
• The Alan Turing Institute Chief Executive, Professor Adrian Smith
• AI for good founder Kriti Sharma
• UKRI chief executive Mark Walport
• Founding Director of the Edinburgh Centre for Robotics, Professor David Lane

British Digital Secretary Jeremy Wright stated: “Britain is already a leading authority in AI. We are home to some of the world’s finest academic institutions, landing record levels of investment to the sector, and attracting the best global tech talent. But we must not be complacent.”

Growing cooperation between the UK and US in a number of technological endeavours could help to harness the strengths of both nations if similarly applied to AI, helping to maintain the countries’ leaderships in the field.

(Photo by Louis Velazquez on Unsplash)

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Of the 30 emerging technologies featured in Gartner’s latest Hype Cycle, nine are directly related to artificial intelligence:

• Generative adversarial networks
• Adaptive machine learning
• Composite AI
• Generative AI
• Responsible AI
• AI-augmented development
• Embedded AI
• Trusted AI
• AI-augmented design

Most of the AI technologies are currently in the initial “Innovation Trigger” part of the Hype Cycle, where excitement builds the fastest.

Responsible AI, AI-augmented development, embedded AI, and Trusted AI, have all now reached the “Peak of Inflated Expectations” and will next move into the dreaded “Trough of Disillusionment” as disappointment sets in over what can realistically be achieved.

Only after the trough, which none of the AI technologies have yet reached, do we head into the areas of the Hype Cycle where adoption occurs with realistic expectations and the productivity rewards are reaped.

Gartner’s Hype Cycle covers the next decade. The current placings of most of the AI technologies on the Hype Cycle indicates that Gartner believes it won’t be until towards the end of the decade we’ll see the most benefits.

Brian Burke, VP of research at Gartner, comments:

“Emerging technologies are disruptive by nature, but the competitive advantage they provide is not yet well known or proven in the market. Most will take more than five years, and some more than 10 years, to reach the Plateau of Productivity.

But some technologies on the Hype Cycle will mature in the near term and technology innovation leaders must understand the opportunities for these technologies, particularly those with transformational or high impact.”

Two technologies which Gartner expects to fast-track through the Hype Cycle are health passports and social distancing technologies, due to their necessity amid the COVID-19 pandemic.

You can find the full Gartner report here (paywall)

(Photo by Verena Yunita Yapi on Unsplash)

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