The ambitious plans were announced this week by President Biden and Japanese Prime Minister Kishida Fumio following Kishida’s Official Visit to the White House.

While the leaders affirmed their commitment across a broad range of areas including defence, climate, development, and humanitarian efforts, the new technology collaborations took centre stage and underscore how the US-Japan alliance is evolving into a comprehensive global partnership underpinned by innovation.

AI takes centre stage

One of the headline initiatives is a $110 million partnership between the University of Washington, University of Tsukuba, Carnegie Mellon University, and Keio University. Backed by tech giants like NVIDIA, Arm, Amazon, and Microsoft—as well as Japanese companies—the program aims to solidify US-Japan leadership in cutting-edge AI research and development.

The US and Japan also committed to supporting each other in establishing national AI Safety Institutes and pledged future collaboration on interoperable AI safety standards, evaluations, and risk management frameworks.

In a bid to mitigate AI risks, the countries vowed to provide transparency around AI-generated and manipulated content from official government channels. Technical research and standards efforts were promised to identify and authenticate synthetic media.

Quantum leaps

Quantum technology featured prominently, with the US National Institute of Standards and Technology (NIST) partnering with Japan’s National Institute of Advanced Industrial Science and Technology (AIST) to build robust quantum supply chains.

Trilateral cooperation between the University of Chicago, University of Tokyo, and Seoul National University was also announced to train a quantum workforce and bolster competitiveness.  

The US and Japan additionally welcomed new commercial deals including Quantinuum providing Japan’s RIKEN institute with $50 million in quantum computing services over five years.

Several semiconductor initiatives were unveiled such as potential cooperation between Japan’s Leading-edge Semiconductor Technology Center (LSTC) with the US National Semiconductor Technology Center and National Advanced Packaging Manufacturing Program. The countries pledged to explore joint semiconductor workforce development initiatives through technical workshops.

Other announced commercial deals spanned cloud computing, telecommunications, batteries, robotics, biotechnology, finance, transportation and beyond—highlighting how the alliance is fusing public and private efforts.

Developing humans

Initiatives around STEM education exchanges, technology curriculums, entrepreneur programs, and talent circulation efforts emphasised the focus on developing human capital to power the coming wave of digital innovation.

While the technological breakthroughs grab attention, the proliferation of initiatives aimed at training, exchanging, and nurturing the innovators, researchers, and professionals across these domains could prove just as vital. The US and Japan appear determined to strategically develop and leverage human resources in lockstep with their efforts to establish cutting-edge AI, quantum, chip, and other advanced tech capabilities.

Both nations clearly recognise that building complementary ecosystems across vital technologies is essential to bolstering competitiveness, economic prosperity, and national security in an era of intensifying strategic competition.

(Photo by Tong Su)

See also: Microsoft AI opens London hub to access ‘enormous pool’ of talent

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

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Held between 15-18 August, the four-day event served as a dynamic platform to spotlight the country’s AI prowess and strengthen the growing partnership between the UK and Thailand.

The week kicked off with a high-level policy roundtable on 15th August at the Eastin Grand Hotel Phayathai, where esteemed experts and policymakers from both nations gathered. 

Distinguished representatives included experts from the Alan Turing Institute, Institute of Analytics, Surrey Institute for People-Centred Artificial Intelligence, and the Foreign, Commonwealth, and Development Office.

Roundtable discussions delved into critical topics such as AI regulation and governance, investment strategies, the potential impact of AI across various sectors, and its role as a force for positive change.

Following the policy roundtable, an AI Week reception was held at the Ambassador’s Residence. The event, attended by UK experts, Thai government officials, and multilateral organisations like UNESCO, provided an opportunity to unveil the UK’s AI strategy and facilitate the establishment of new relationships between stakeholders.

A highlight of the week was the participation of nine British AI and data businesses in the GREAT Pavilion at the TechSauce Global Summit, held at the Queen Sirikit National Convention Centre from August 16 to 17.

The summit drew over 15,000 tech experts, investors, and businesses, and showcased the diverse commercial capabilities of UK enterprises in sectors spanning agriculture, finance, climate, design, and cybersecurity.

Attendees had the chance to engage in workshops led by UK experts, covering AI applications in health, public services, and national AI strategy development. British businesses and experts also contributed to discussions on topics like climate tech, femtech, and air quality, and presented their innovations to local venture capitalists.

Natalie Black, His Majesty’s Trade Commissioner for Asia Pacific, expressed enthusiasm about the event, saying, “With over 160 unicorns and a tech sector worth $1 trillion, the UK looks forward to offering our expertise and partnership in developing AI for good. I am excited to see deepening tech collaborations between our two countries.”

The week’s activities concluded with the ‘Turing Night’ event on 18th August at Icon Siam. The event, attended by over 150 government officials, tech businesses, and investors, featured an exhibition, networking reception, panel discussion on AI, and a screening of the film ‘The Imitation Game’.

David Thomas, British Charge d’Affaires to Thailand, remarked, “UK AI Week epitomises the modern spirit of collaboration between the UK and Thailand, fostering innovation and advancing free and ethical AI development.”

The success of the UK AI Week in Bangkok underscores the UK’s dedication to technological advancement and international collaboration in shaping the future of AI for the betterment of society.

Solidifying its global AI leadership, the UK will hold a ‘Safety Summit’ in November. The event will be held at Bletchley Park, home to the infamous codebreakers of World War Two —including, of course, Alan Turing.

The UK Government also announced a £100 million fund last week that will be used to bolster the production of homegrown AI chips.

The UK is home to a handful of relevant established companies like Arm – in addition to promising startups like Graphcore – but most firms operating in the country are US-based. The government hopes the fund will help to boost the UK’s position in AI hardware production although experts have expressed concern the pot is far too low in comparison to peers.

(Photo by Braden Jarvis on Unsplash)

See also: UK Deputy PM: AI is the most ‘extensive’ industrial revolution yet

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with Digital Transformation Week.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

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Sure. We’re a 30-year-old global IT services and professional services provider. We specialise in using emerging state-of-the-art technologies, such as artificial intelligence, big data and blockchain, to solve real business problems. We’re highly obsessed with our customers, about their problems – not about technologies – although we are technology experts. But we always try to find the best technology that will help our customers get to the point where they want to be. 

So we’ve been in the market for quite a while, having originated in Ukraine. But now we have offices all over the globe – US, Latin America, Singapore, Middle East, all over Europe – and we operate in multiple industries. We have some specialised leadership around specific industries, such as retail, financial services, healthcare, energy, oil and gas, and manufacturing. We also work with a lot of digital natives and independent software vendors, helping them adopt this technology in their products, so that they can better serve their customers.

What are the main trends you’ve noticed developing in AI and machine learning?

One of the biggest trends is that, while people used to question whether AI, machine learning and data science are the technologies of the future; that’s no longer the question. This technology is already everywhere. And the vast majority of the innovation that we see right now wouldn’t have been possible without these technologies. 

One of the main reasons is that this tech allows us to address and solve some of the problems that we used to consider intractable. Think of natural language, image recognition or code generation, which are not only hard to solve, they’re also hard to define. And approaching these types of problems with our traditional engineering mindset – where we essentially use programming languages – is just impossible. Instead, we leverage the knowledge stored in the vast amounts of data we collect, and use it to find solutions to the problems we care about. This approach is now called Machine Learning, and it is the most efficient way to address those types of problems nowadays.

But with the amount of data we can now collect, the compute power available in the cloud, the efficiency of training and the algorithms that we’ve developed, we are able to get to the stage where we can get superhuman performance with many tasks that we used to think only humans could perform. We must admit that human intelligence is limited in capacity and ability to process information. And machines can augment our intelligence and help us more efficiently solve problems that our brains were not designed for.

The overall trend that we see now is that machine learning and AI are essentially becoming the industry standard for solving complex problems that require knowledge, computation, perception, reasoning and decision-making. And we see that in many industries, including healthcare, finance and retail.

There are some more specific emerging trends. The topic of my TechEx North America keynote will be about generative AI, which many folk might think is something just recently invented, something new, or they may think of it as just ChatGPT. But these technologies have been evolving for a while. And we, as hands-on practitioners in the industry, have been working with this technology for quite a while. 

What has changed now is that, based on the knowledge and experience we’ve collected, we were able to get this tech to a stage where GenAI models are useful. We can use it to solve some real problems across different industries, from concise document summaries to advanced user experiences, logical reasoning and even the generation of unique knowledge. That said, there are still some challenges with reliability, and understanding the actual potential of these technologies.

How important are AI and machine learning with regards to product innovation?

AI and Machine Learning essentially allow us to address the set of problems that we can’t solve with traditional technology. If you want to innovate, if you want to get the most out of tech, you have to use them. There’s no other choice. It’s a powerful tool for product development, to introduce new features, for improving customer user experiences, for deriving some really deep actionable insights from the data. 

But, at the same time, it’s quite complex technology. There’s quite a lot of expertise involved in applying this tech, training these types of models, evaluating them, deciding what model architecture to use, etc. And, moreover, they’re highly experiment driven, meaning that in traditional software development we often know in advance what to achieve. So we set some specific requirements, and then we write a source code to meet those requirements. 

And that’s primarily because, in traditional engineering, it’s the source code that defines the behaviour of our system. With machine learning and artificial intelligence the behaviour is defined by the data, which means that we hardly ever know in advance what the quality of our data is. What’s the predictive power of our data? What kind of data do we need to use? Whether the data that we collected is enough, or whether we need to collect more data. That’s why we always need to experiment first. 

But I think, in some way, we got used to the uncertainty in the process and the outcomes of AI initiatives. The AI industry gave up on the idea that machine learning will be predictable at some point. Instead, we learned how to experiment efficiently, turning our ideas into hypotheses that we can quickly validate via experimentation and rapid prototyping, and evolving the most successful experiments into full-fledged products. That’s essentially what the modern lifecycle of AI/ML products looks like.

It also requires the product teams to adopt a different mindset of constant ideation and experimentation, though. It starts with selecting those ideas and use cases that have the highest potential, the most feasible ones that may have the biggest impact on the business and the product. From there, the team can ideate around potential solutions, quickly prototyping and selecting those that are most successful. That requires experience in identifying the problems that can benefit from AI/ML the most, and agile, iterative processes of validating and scaling the ideas.

How can businesses use that type of technology to improve personalisation?

That’s a good question because, again, there are some problems that are really hard to define. Personalisation is one of them. What makes me or you a person? What contributes to that? Whether it’s our preferences. How do we define our preferences? They might be stochastic, they might be contextual. It’s a highly multi dimensional problem. 

And, although you can try to approach it with a more traditional tech, you’ll still be limited in that capacity – depths of personalisation that you may get. The most efficient way is to learn those personal signals, preferences from the data, and use those insights to deliver personalised experiences, personalised marketing, and so on. 

Essentially, AI/ML acts as a sort of black box between the signal and the user and specific preferences, specific content that would resonate with that specific user. As of right now, that’s the most efficient way to achieve personalisation. 

One other benefit of modern AI/ML is that you can use various different types of data. You can combine clickstream data from your website, collecting information about how users behave on your website. You can collect text data from Twitter or any other sources. You can collect imagery data, and you can use all that information to derive the insights you care about. So the ability to analyse that heterogeneous set of data is another benefit that AI/ML brings into this game.

How do you think machine learning is impacting the metaverse and how are businesses benefiting from that?

There are two different aspects. ‘Metaverse’ is quite an abstract term, and we used to think of it from two different perspectives. One of them is that you want to replicate your physical assets – part of our physical world in the metaverse. And, of course, you can try to approach it from a traditional engineering standpoint, but many of the processes that we have are just too complex. It’s really hard to replicate them in a digital world. So think of a modern production line in manufacturing. In order for you to have a really precise, let’s call it a digital twin, of some physical assets, you have to be smart and use something that will allow you to get as close as possible in your metaverse to the physical world. And AI/ML is the way to go. It’s one of the most efficient ways to achieve that.

Another aspect of the metaverse is that since it’s digital, it’s unlimited. Thus, we may also want to have some specific types of assets that are purely digital, that don’t have any representation in the real world. And those assets should have similar qualities and behaviour as the real ones, handling a similar level of complexity. In order to program these smart, purely digital processes or assets, you need AI and ML to make them really intelligent.

Are there any examples of companies that you think have been utlising AI and machine learning well?

There are the three giants – Facebook, Google, Amazon. All of them are essentially a key driver behind the industry. And the vast majority of their products are, in some way, powered by AI/ML. Quite a lot has changed since I started my career but, even when I joined SoftServe around 10 years ago, there was a lot of research going on into AI/ML. 

There were some big players using the technology, but the vast majority of the market were just exploring this space. Most of our customers didn’t know anything about it. Some of the first questions they had were ‘can you educate us on this? What is AI/ML? How can we use it?’ 

What has changed now is that almost any company we interact with has already done some AI/ML work, whether they build something internally or they use some AI/ML products. So the perception has changed.

The overall adoption of this technology now is at the scale where you can find some aspects of AI/ML in almost any company.

You may see a company that does a lot of AI/ML on their, let’s say, marketing or distribution, but they have some old school legacy technologies in their production site or in their supply chain. The level of AI/ML adoption may differ across different lines of business. But I think almost everyone is using it now. Even your phone, it’s backed with AI/ML features. So it’s hard to think of a company that doesn’t use any AI/ML right now.

Do you think, in general, companies are using AI and machine learning well? What kind of challenges do they have when they implement it?

That’s a good question. The main challenge of applying these technologies today is not how to be successful with this tech, but rather how to be efficient. With the amount of data that we have now, and data that the companies are collecting, plus the amount of tech that is open source or publicly available – or available as managed services from AWS, from GCP – it’s easy to get some good results.

The question is, how do you decide where to apply this technology? How efficiently can you identify those opportunities, and find the ones that will bring the biggest impact, and can be implemented in the most time-efficient and cost-effective manner? 

Another aspect is how do you quickly turn those ideas into production-grade products? It’s a highly experiment-driven area, and there is a lot of science, but you still need to build reliable software on the research results. 

The key drivers for successful AI adoption are finding the right use cases where you can actually get the desired outcomes in the most efficient way, and turn ideas into full-fledged products. We’ve seen some really innovative companies that had brilliant ideas. They may have built some proof of concepts around their ideas, but they didn’t know how to evolve or how to build reliable products out of it. At the same time, there are some technically savvy and digitally native companies. They have tonnes of smart engineers, but they don’t have the right expertise and experience in AI/ML technologies. They don’t know how to apply this tech to real business problems, or what low-hanging fruits are available to them. They just struggle with finding the best way to leverage this tech.

What do you think the future holds for AI and machine learning?

I generally try to be more optimistic about the future because there are obviously a lot of fears around AI/ML. And I think that’s quite natural. If you look back in history, it was the same with electricity and any other innovative technologies.

One of the fears that I think does have some merit is that this technology may replace some real jobs. I think that’s a bit of a pessimistic view because history also teaches us that whatever technology we get, we still need that human aspect to it. 

Almost all the technology that we use right now augments our intelligence. It does not replace it. And I think that the future of AI will be used in a cooperative way. If you’ve seen products like GitHub Copilot, the purpose of this product is essentially to assist the developer in writing code. We still can’t use AI to write entire programs. We need a human to guide that AI to our desired outcome. What exactly do we want to achieve? What is our objective? What is our user expectation?

Similarly, maybe this technology will be applied to a broader set of use cases where AI will be assisting us, not replacing us. There is a quote that I wish was mine but I still think it’s a very good way of thinking about the role of AI: if you think that AI will replace you or your job, most likely you’re wrong. It’s the people who will be using AI who will replace you at your job. 

So I think one of the most important skills to learn right now is how to leverage this tech to make your work more efficient. And that should help many people get that competitive advantage in the future.

• Iurii Milovanov is the director of AI and data science at SoftServe, a technology company specialising in consultancy services and software development. 

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The event is co-located with Digital Transformation Week.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

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Michelle Donelan, Science, Innovation, and Technology Secretary, said: “AI has the potential to make Britain a smarter, healthier and happier place to live and work. Artificial intelligence is no longer the stuff of science fiction, and the pace of AI development is staggering, so we need to have rules to make sure it is developed safely.

“Our new approach is based on strong principles so that people can trust businesses to unleash this technology of tomorrow.”

The framework, set out in the AI regulation white paper, is based on these five principles:

• Safety – Ensuring that applications function in a secure, safe, and robust manner.
• Transparency and explainability – Organisations that deploy AI should communicate when and how it’s used. Furthermore, they should be able to explain a system’s decision-making process.
• Fairness – Ensure compatibility with the UK’s existing laws, including the Equality Act 2010 and UK GDPR.
• Accountability and governance – Introducing measures to ensure appropriate oversight of AI.
• Contestability and redress – Ensure that people have clear routes to dispute outcomes or decisions generated by AI.

The principles will be applied by existing regulators in their sectors rather than through the creation of a single new regulator. The government has allocated £2m ($2.7m) to fund an AI sandbox, where businesses can test AI products and services.

Over the next year, regulators will issue guidance to organisations and other resources to implement the principles. Legislation could also be introduced to ensure the principles are considered consistently.

A consultation has also been launched by the government on new processes to improve coordination between regulators and to evaluate the effectiveness of the framework.

Emma Wright, Head of Technology, Data, and Digital at law firm Harbottle & Lewis, commented:

“I do welcome industry-specific regulation rather than primary legislation covering AI  (such as the EU is proposing). However, I am concerned that this is essentially another consultation paper calling for regulators to produce more guidance when entrepreneurs and investors are looking for greater regulatory certainty. 

The use of AI is becoming mainstream with the arrival of ChatGPT and not enough attention has been given to the need for capacity building within the existing regulators who will now be tasked with driving responsible innovation whilst not stifling investment. 

Building trustworthy AI will be the key to greater adoption and setting basic frameworks for entrepreneurs and investors to operate is not at odds with this. Although regulatory sandboxes have been successfully used in the past in other tech verticals, such as fintech, the issue is that lots of the AI tools currently being released have unintended consequences when made available for general use – it seems hard to see how a true sandbox environment will be able to replicate such scenarios and risks damaging any trust users place in an AI tool that has been sandboxed but produces discriminatory results or output.

It is possible to have a pro-innovation approach while setting basic frameworks to be followed such as the UNESCO Recommendation on Ethical AI (that the UK is a signatory to) and it feels like a little bit of a missed opportunity to have missed aligning a pro-innovation environment with what responsible AI use means today rather than at some point in the future.”

The UK’s AI industry currently employs over 50,000 people and contributed £3.7bn to the economy in 2022. Britain is home to twice as many companies offering AI services and products as any other European country, with hundreds of new firms created each year.

Behind the US and China, the UK’s tech sector overall has the third-highest amount of VC investment in the world – more than Germany and France combined – and has produced more than double the number of $1 billion tech firms than any other European country.

However, concerns have been raised that AI could pose risks to privacy, human rights, and safety, as well as the fairness of using AI tools to make decisions that affect people’s lives, such as assessing loan or mortgage applications.

The proposals in the white paper aim to address these concerns and have been warmly welcomed by businesses, which previously called for more coordination between regulators to ensure effective implementation across the economy.

Lila Ibrahim, COO at DeepMind, commented: “AI has the potential to advance science and benefit humanity in numerous ways, from combating climate change to better understanding and treating diseases. This transformative technology can only reach its full potential if it is trusted, which requires public and private partnership in the spirit of pioneering responsibly.

“The UK’s proposed context-driven approach will help regulation keep pace with the development of AI, support innovation, and mitigate future risks.”

Grazia Vittadini, CTO at Rolls-Royce, added: “Both our business and our customers will benefit from agile, context-driven AI regulation.

“It will enable us to continue to lead the technical and quality assurance innovations for safety-critical industrial AI applications, while remaining compliant with the standards of integrity, responsibility, and trust that society demands from AI developers.”

The new framework aims to provide protections for the public without stifling the use of AI in developing the economy, better jobs, and new discoveries.

Separately, an open letter posted today – signed by Elon Musk, Steve Wozniak, and over 1,000 other experts – called for a halt to “out-of-control” AI development.

You can find a full copy of the UK’s AI regulation whitepaper here.

(Photo by Steve Harvey on Unsplash)

Related: Editorial: UK puts AI at the centre of its Budget

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The event is co-located with Digital Transformation Week.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

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Research from GlobalData has found that – of the 45 international AI unicorns identified – China has the largest share with 19 based in the country.

Priya Toppo, Analyst of Thematic Research at GlobalData, comments:

“China is a leading player in AI, with a number of established companies such as Baidu, Hikvision, iFlytek, Tencent, and Alibaba.

The country also has a strong AI startup ecosystem, which is evident from the large number of AI unicorns (privately held startup valued at  $1bn or more).” 

Collectively, the Chinese AI unicorns are valued at $43.5 billion.

Beijing has been on a regulatory crackdown in recent months, especially on Chinese companies doing business in, and with, the US.

Robotaxi firm Didi, for example, was targeted by Chinese authorities following its $4.4 billion listing on the New York Stock Exchange (NYSE). Chinese regulators forced Apple to remove Didi from the App Store while other app stores operating in China have also been ordered not to serve Didi’s app.

Despite the crackdowns, AI development in China has remained strong.

Ms Toppa added: “Chinese AI companies have faced several regulatory challenges but have emerged even stronger. The companies are well placed in key themes such as computer vision, deep learning technologies, smart cities, and autonomous vehicles.” 

SenseTime remains China’s biggest AI unicorn with a $12 billion valuation and total funding to date of $2.6 billion. This is followed by CloudWalk’s $3.3 billion valuation and $500 million total funding so far.

CloudWalk recently led in a facial recognition test conducted by the US government’s National Institute of Standards and Technology. However, CloudWalk has faced serious allegations including from US officials who declared the firm was “complicit in human rights violations and abuses committed in China’s campaign of repression, mass arbitrary detention, forced labour and high-technology surveillance against Uighurs, ethnic Kazakhs, and other members of Muslim minority groups in the Xinjiang Uighur Autonomous Region”.

Other notable Chinese AI unicorns identified in GlobalData’s research include Horizon Robotics with a valuation of $5 billion, Megvii with a valuation of $4 billion, and Yitu Technology with a valuation of $2.2 billion.

“The Chinese AI ecosystem will become even stronger as leading companies go public and are able to invest the significant amounts of new capital raised,” Ms Toppo concludes.

China’s growing dominance in AI has some international competitors worried they’ll be unable to keep pace.

Nicolas Chaillan, the Pentagon’s former chief software officer, recently resigned in protest after claiming the US has “no competing fighting chance against China in 15 to 20 years” when it comes to AI and cyberwarfare.

Chaillan says that China is set to dominate the future of the world, controlling everything from media narratives to geopolitics.

Part of the problem, Chaillan believes, is the reluctance of US companies such as Google to work with the government on AI due to ethical debates over the technology. In contrast, Chinese firms are obligated to work with their government and have little regard for ethics.

(Photo by James Lee on Unsplash)

Find out more about Digital Transformation Week North America, taking place on 9-10 November 2021, a virtual event and conference exploring advanced DTX strategies for a ‘digital everything’ world.

The post GlobalData: China is ahead of global rivals for AI ‘unicorns’ appeared first on AI News.

Closer to home, the UK is about to get back into the space arena with UK Transport Secretary Grant Shapps announcing in May this year that rockets will be able to launch from the UK in 2022, with spaceports planned in Cornwall, Scotland and Wales.

There are some big takeaways for product innovators and business leaders in this – pardon the pun – space. 

Investments galore!

The advent of SpaceX, Blue Origin, and Virgin Galactic have generated a highly entrepreneurial, private sector-led new space technology ecosystem, with startups offering services and applications that are highly innovative and customer-focused. Morgan Stanley, which predicts the global space industry will generate revenue upwards of £730B in 2040, identifies this as a big investment opportunity for venture capital and private equity.

In Q2 of 2021, new space exploration received an infusion of £3.3B, setting it on track to beat 2020’s total investment of £6.6B, reports Space Capital, a venture capital firm focused on the sector. The report estimates a total of £145B of equity investment across 1,533 companies in the new space ecosystem over the last decade. 

By factoring in the product innovations downstream that impact our daily life on Earth thanks to technology transfer, there is good reason for this optimism.

Meanwhile, UK tech innovation continues to grow. In the Future UK Tech Built tech nation report 2021, UK tech VC investment is third in the world, hitting a record high of £10B in 2020 in the face of challenging conditions. UK deep tech investment also rose by 17% in 2020, the highest rate of growth globally.

In the UK, the space industry is on an upward trajectory. Research findings from the latest ‘Size and Health of the UK Space Industry’ report, commissioned by the UK Space Agency shows the sector supports a highly skilled and productive workforce that’s growing across the country.

The report shows in 2018/2019 income has risen from £14.8 billion to £16.4 billion, representing a growth of 5.7% (or 2.8% per annum) in real terms and employment is up by 3,200 from 41,900 to 45,100. 

In many ways, the new space ecosystem and its constellation of space tech startups and unicorns, ready to boldly go where no one has gone before, follow three of the ‘golden rules of product innovation’ in order to strive toward.

Rule 1: Shoot for the moon and aim for radical changes

SpaceX, Blue Origin, and Virgin Galactic have ushered in radical and disruptive innovation into the aerospace and space technology sector. SpaceX was listed as the top disrupter on the CNBC Disruptor 50 List in 2018, upending both aerospace leader Boeing and the rocket industry with its reusable rockets, becoming one of the most valuable companies in the world.  

Product leaders might typically be inclined to go for incremental innovation because it appears sustainable. However, your product could miss the mark, lose product/market fit, and eventually, customers if you don’t innovate quickly. During a crisis recovery period, this is of critical importance, reports McKinsey. 

Their recent survey of more than 200 executives revealed that over 85% think that the pandemic will have a lasting impact on customer needs over the next five years, but only 21% report they have the commitment and resources to face the challenge.

By following agile, data-informed methods, product leaders can test and iterate while keeping management informed with a high-level road map. By moving fast, with analytics at speed and reduced time to insights, product leaders can innovate to stay ahead of the competition.

Rule 2: Remove hurdles to allow space travel

Virgin Galactic and Blue Origin have said they want to lower barriers to mass adoption of space travel. Space X has stated its single vision of reducing the cost to launch. While these sound like lofty aims, they follow the right questions and have an impact on technological progress on the ground. Cost of launch is the crucial barrier to product innovation in space technology, and reusable rockets pave the way for new space pioneers to experiment, test, iterate, and launch products and technology more frequently.

Product leaders are equally focused on mass adoption of their products. A key “reduction of cost to launch” on that path is with the use of white-labelled embedded analytics. Think about how you can make it easier for your customers to interact with their data on your app. What if they could simply ask a question using plain language and have your app present analysed insights in a user-friendly format? That is now an attainable differentiator and value proposition for customers to keep returning to your product.

Rule 3: Take advantage of data to make informed decisions 

Virgin Galactic leader Richard Branson and Blue Origin chief Jeff Bezos are bringing their famous customer and data obsession to a sector that traditionally focused on technology first, and the user experience next, with little incentive for change. New space startups and their backers, however, expect profitability from innovation, and have a laser-sharp focus on customer-centric innovation. 

For example, Virgin Galactic’s stated customer goal for its fully crewed test flight on July 11 was to assess everything from the seat comfort to the weightlessness experience, aiming to ensure the customer experience of the complete wonder and awe of space travel.

Product leaders are customer-obsessed but often rely on instinct rather than quantifiable data to drive their innovation decisions, missing opportunities to win customers. Bring that back into your court by using data derived from continuous testing so you can arrive at a solution that meets customer needs and ensures product stickiness.

For example, offering traditional reporting tools to your customers with static visualisations and dashboards simply dumps metrics onto your customers rather than providing actionable insights, leading to dissatisfaction and poor engagement. 

Instead serve contextual insights to your customers in a revolutionary way, by embedding them into your products to achieve a seamless and intuitive user experience. At Sisense, it is called infused analytics, and it empowers your customers with actionable insights where they spend the most time, in their communication apps or CRMs.

You can further enhance user experience with native app visual interfaces that help customers take action on their insights without jumping to and from workflows. By going beyond data delivery to make analytics an intuitive and integral part of decision making, product leaders can innovate to make a difference.

The sky is no longer the limit

Product innovation is arguably rocket science. Just as they did with the new space race, scientists must think big, ask the right questions, and constantly test. And product leaders must aim beyond the stars to create visionary products with long-lasting and universal impact.

With the UK preparing to relaunch itself into the stratosphere, the availability of reliable quantifiable data and the increase in investments – anything is possible. 

Editor’s note: This article is in association with Sisense

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