Ditched Google for DuckDuckGo...

When you realise that most things you search for online are really boring and obvious, you soon realise you don't really need Google in your life.

I ditched Google for DuckDuckGo. Here's why you should too
By James Temperton
WIRED, 24 November 2019

What was the last thing you searched for online? For me, it was ‘$120 in pounds’. Before that, I wanted to know the capital of Albania (Tirana), the Twitter handle of Liberal Democrat deputy leader Ed Davey (he’s @EdwardJDavey) and dates of bank holidays in the UK for 2019 (it’s a late Easter next year, folks). Thrilling, I’m sure you’ll agree. But something makes these searches, in internet terms, a bit unusual. Shock, horror, I didn’t use Google. I used DuckDuckGo. And, after two years in the wilderness, I’m pretty sure I’m sold on a post-Google future.

It all started with a realisation: most the things I search for are easy to find. Did I really need the all-seeing, all-knowing algorithms of Google to assist me? Probably not. So I made a simple change: I opened up Firefox on my Android phone and switched Google search for DuckDuckGo. As a result, I’ve had a fairly tedious but important revelation: I search for really obvious stuff. Google’s own data backs this up. Its annual round-up of the most searched-for terms is basically a list of names and events: World Cup, Avicii, Mac Miller, Stan Lee, Black Panther, Megan Markle. The list goes on. And I don’t need to buy into Google’s leviathan network of privacy-invading trackers to find out what Black Panther is and when I can go and see it at my local cinema.

While I continue to use Google at work (more out of necessity as my employer runs on G-Suite), on my phone I’m all about DuckDuckGo. I had, based on zero evidence, convinced myself that finding things on the internet was hard and, inevitably, involved a fair amount of tracking. After two years of not being tracked and targeted I have slowly come to realise that this is nonsense.

DuckDuckGo works in broadly the same way as any other search engine, Google included. It combines data from hundreds of sources including Wolfram Alpha, Wikipedia and Bing, with its own web crawler, to surface the most relevant results. Google does exactly the same, albeit on a somewhat larger scale. The key difference: DuckDuckGo does not store IP addresses or user information.

Billed as the search engine that doesn’t track you, DuckDuckGo processes around 1.5 billion searches every month. Google, for contrast, processes around 3.5 billion searches per day. It’s hardly a fair fight, but DuckDuckGo is growing. Back in 2012, it averaged just 45 million searches per month. While Google still operates in a different universe, the actual difference in the results you see when you search isn’t so far apart. In fact, in many respects, DuckDuckGo is better. Its search results aren’t littered with Google products and services – boxes and carousels to try and persuade people to spend more time in Google’s family of apps.

Search for, say, ‘Iron Man 2’ and Google will first tell you it can be purchased from Google Play or YouTube from £9.99. It will then suggest you play a trailer for the film on, where else, YouTube. The film is also “liked” by 92 per cent of Google users and people searching for this also search for, you guessed it, Iron Man and Iron Man 3. The same search on DuckDuckGo pulls in a snippet from Wikipedia and quick links to find out more on IMDb, Rotten Tomatoes, Amazon or iTunes. For the most part, the top of Google’s page of results directs you towards more Google products and services.

Go further still and search for ‘Iron Man 2 cast’ and Google displays a carousel of names and pictures right at the top of the page. As a result, 50 per cent of all Google searches now end without a click. Great for Google, bad for the list of websites below that also contain this information and that you will never visit. Do the same search on DuckDuckGo and the top result is IMDb. It might sound small but issues like this are fundamental to how the internet works – and who makes the most money from it. Google’s prioritisation of its results, and a perceived bias towards its own products and services, has landed the company in hot water with the European Commission slapping it with multi-billion pound fines and launching investigation after investigation into alleged anti-competitive behaviour. What’s good for Google, the commission argues, isn’t necessarily good for consumers or competitors.

Then there’s privacy. Search for something on DuckDuckGo and, for the most part, you just get a list of links or a simple snippet with exactly the information you were looking for. And it does all this without storing or tracking my search history. Nor is what I search for collected and shared with advertisers, allowing them to micro-target me with a myriad of things I’m never likely to buy. The ads I do see in DuckDuckGo, which the company explains makes it more than enough money to operate, are more general. My search for bank holidays in the UK returned an advert for a package holiday company.

A quick office survey revealed similar search banality: recent Googles included ‘capitalist’, ‘toxoplasmosis’ and ‘hyde park police’. For the most part, what we’re looking for online is simple: it’s definitions, companies, names and places. Where DuckDuckGo has struggled is when I look for something incredibly specific. So, for example, search for ‘film Leonardo Dicaprio goats scene’ in DuckDuckGo and it doesn’t work out you’re looking for Blood Diamond. Google does. While Google, with its vastly greater tranche of search data, is able to second-guess what I’m after, DuckDuckGo requires a bit more hand-holding. That doesn’t mean I can’t find what I’m looking for, but it does mean I have to modify my search term a couple of times to narrow things down.

But such moments are rare and fleeting. Yes, Google has more bells and whistles. But such bells and whistles are, once you stop seeing them, easily forgotten. A realisation that most of your online searches are really bloody obvious is somewhat liberating. You don’t need to be tracked and targeted to work out the name of that hideous earworm that’s been stuck in your head all day (in my case it was Vanessa Carlton’s A Thousand Miles, I don’t know why). DuckDuckGo helps you find it in the same way Google does: you tap in a random line of lyrics, it finds them on a site with song lyrics on it and voila, the earworm is dead.

It’s not a fair fight, but it is one, oddly, where the small guy can compete. It might seem ludicrous – DuckDuckGo has 78 employees and Google 114,096 – but often the outcome is the same. For the majority of your searches David, it turns out, is just as good as Goliath.

Artificial Intelligence (AI) Essential Info...

Artificial intelligence: Cheat sheet
by Brandon Vigliarolo
Tech Republic, October 23, 2020

Learn artificial intelligence basics, business use cases, and more in this beginner's guide to using AI in the enterprise.

Artificial intelligence (AI) is the next big thing in business computing. Its uses come in many forms, from simple tools that respond to customer chat, to complex machine learning systems that predict the trajectory of an entire organization. Popularity does not necessarily lead to familiarity, and despite its constant appearance as a state-of-the-art feature, AI is often misunderstood.

In order to help business leaders understand what AI is capable of, how it can be used, and where to begin an AI journey, it's essential to first dispel the myths surrounding this huge leap in computing technology.

What is artificial intelligence?

When AI comes to mind, it's easy to get pulled into a world of science-fiction robots like Data from Star Trek: The Next Generation, Skynet from the Terminator series, and Marvin the paranoid android from The Hitchhiker's Guide to the Galaxy.

The reality of AI is nothing like fiction, though. Instead of fully autonomous thinking machines that mimic human intelligence, we live in an age where computers can be taught to perform limited tasks that involve making judgments similar to those made by people, but are far from being able to reason like human beings.

Modern AI can perform image recognition, understand the natural language and writing patterns of humans, make connections between different types of data, identify abnormalities in patterns, strategize, predict, and more.

All artificial intelligence comes down to one core concept: Pattern recognition. At the core of all applications and varieties of AI is the simple ability to identify patterns and make inferences based on those patterns.

AI isn't truly intelligent in the way we define intelligence: It can't think and lacks reasoning skills, it doesn't show preferences or have opinions, and it's not able to do anything outside of the very narrow scope of its training.

That doesn't mean AI isn't useful for businesses and consumers trying to solve real-world problems, it just means that we're nowhere close to machines that can actually make independent decisions or arrive at conclusions without being given the proper data first. Artificial intelligence is still a marvel of technology, but it's still far from replicating human intelligence or truly intelligent behavior.

What can artificial intelligence do?

AI's power lies in its ability to become incredibly skilled at doing the things humans train it to. Microsoft and Alibaba independently built AI machines capable of better reading comprehension than humans, Microsoft has AI that is better at speech recognition than its human builders, and some researchers are predicting that AI will outperform humans in most everything in less than 50 years.

That doesn't mean those AI creations are truly intelligent--only that they're capable of performing human-like tasks with greater efficiency than us error-prone organic beings. If you were to try, say, to give a speech recognition AI an image-recognition task, it would fail completely. All AI systems are built for very specific tasks, and they don't have the capability to do anything else.

Since the COVID-19 pandemic began in early 2020, artificial intelligence and machine learning has seen a surge of activity as businesses rush to fill holes left by employees forced to work remotely, or those who've lost jobs due to the financial strain of the pandemic.

The quick adoption of AI during the pandemic highlights another important thing that AI can do: Replace human workers. According to Gartner, 79% of businesses are currently exploring or piloting AI projects, meaning those projects are in the early post-COVID-19 stages of development. What the pandemic has done for AI is cause a shift in priorities and applications: Instead of focusing on financial analysis and consumer insight, post-pandemic AI projects are focusing on customer experience and cost optimization, Algorithmia found.

Like other AI applications, customer experience and cost optimization are based on pattern recognition. In the case of the former, AI bots can perform many basic customer service tasks, freeing employees up to only address cases that need human intervention. AI like this has been particularly widespread during the pandemic, when workers forced out of call centers put stress on the customer service end of business.

What are the business applications of artificial intelligence?

Modern AI systems are capable of amazing things, and it's not hard to imagine what kind of business tasks and problem solving exercises they could be suited to. Think of any routine task, even incredibly complicated ones, and there's a possibility an AI can do it more accurately and quickly than a human--just don't expect it to do science fiction-level reasoning.

In the business world, there are plenty of AI applications, but perhaps none is gaining traction as much as business analytics and its end goal: Prescriptive analytics.

Business analytics is a complicated set of processes that aim to model the present state of a business, predict where it will go if kept on its current trajectory, and model potential futures with a given set of changes. Prior to the AI age, analytics work was slow, cumbersome, and limited in scope.

When modeling the past of a business, it's necessary to account for nearly endless variables, sort through tons of data, and include all of it in an analysis that builds a complete picture of the up-to-the-present state of an organization. Think about the business you're in and all the things that need to be considered, and then imagine a human trying to calculate all of it--cumbersome, to say the least.

Predicting the future with an established model of the past can be easy enough, but prescriptive analysis, which aims to find the best possible outcome by tweaking an organization's current course, can be downright impossible without AI help.

There are many artificial intelligence software platforms and AI machines designed to do all that heavy lifting, and the results are transforming businesses: What was once out of reach for smaller organizations is now feasible, and businesses of all sizes can make the most of each resource by using artificial intelligence to design the perfect future.

Analytics may be the rising star of business AI, but it's hardly the only application of artificial intelligence in the commercial and industrial worlds. Other AI use cases for businesses include the following.

•     Recruiting and employment: Human beings can often overlook qualified candidates, or candidates can fail to make themselves noticed. Artificial intelligence can streamline recruiting by filtering through larger numbers of candidates more quickly, and by noticing qualified people who may go overlooked.
•     Fraud detection: Artificial intelligence is great at picking up on subtle differences and irregular behavior. If trained to monitor financial and banking traffic, AI systems can pick up on subtle indicators of fraud that humans may miss.
•     Cybersecurity: Just as with financial irregularities, artificial intelligence is great at detecting indicators of hacking and other cybersecurity issues.
•     Data management: Using AI to categorize raw data and find relations between items that were previously unknown.
•     Customer relations: Modern AI-powered chatbots are incredibly good at carrying on conversations thanks to natural language processing. AI chatbots can be a great first line of customer interaction.
•     Healthcare: Not only are some AIs able to detect cancer and other health concerns before doctors, they can also provide feedback on patient care based on long-term records and trends.
•     Predicting market trends: Much like prescriptive analysis in the business analytics world, AI systems can be trained to predict trends in larger markets, which can lead to businesses getting a jump on emerging trends.
•     Reducing energy use: Artificial intelligence can streamline energy use in buildings, and even across cities, as well as make better predictions for construction planning, oil and gas drilling, and other energy-centric projects.
•     Marketing: AI systems can be trained to increase the value of marketing both toward individuals and larger markets, helping organizations save money and get better marketing results.

If a problem involves data, there's a good possibility that AI can help. This list is hardly complete, and new innovations in AI and machine learning are being made all the time.

What AI platforms are available?

When adopting an AI strategy, it's important to know what sorts of software are available for business-focused AI. There are a wide variety of platforms available from the usual cloud-hosting suspects like Google, AWS, Microsoft, and IBM, and choosing the right one can mean the difference between success and failure.

AWS Machine Learning offers a wide variety of tools that run in the AWS cloud. AI services, pre-built frameworks, analytics tools, and more are all available, with many designed to take the legwork out of getting started. AWS offers pre-built algorithms, one-click machine learning training, and training tools for developers getting started in, or expanding their knowledge of AI development.

Google Cloud offers similar AI solutions to AWS, as well as having several pre-built total AI solutions that organizations can (ideally) plug into their organizations with minimal effort. Google's AI offerings include the TensorFlow open source machine learning library.

Microsoft's AI platform comes with pre-generated services, ready-to-deploy cloud infrastructure, and a variety of additional AI tools that can be plugged in to existing models. Its AI Lab also offers a wide range of AI apps that developers can tinker with and learn from what others have done. Microsoft also offers an AI school with educational tracks specifically for business applications.

Watson is IBM's version of cloud-hosted machine learning and business AI, but it goes a bit further with more AI options. IBM offers on-site servers custom built for AI tasks for businesses that don't want to rely on cloud hosting, and it also has IBM AI OpenScale, an AI platform that can be integrated into other cloud hosting services, which could help to avoid vendor lock-in.

Before choosing an AI platform, it's important to determine what sorts of skills you have available within your organization, and what skills you'll want to focus on when hiring new AI team members. The platforms can require specialization in different sorts of development and data science skills, so be sure to plan accordingly.

What AI skills will businesses need to invest in?

With business AI taking so many forms, it can be tough to determine what skills an organization needs to implement it.

As previously reported by TechRepublic, finding employees with the right set of AI skills is the problem most commonly cited by organizations looking to get started with artificial intelligence.

Skills needed for an AI project differ based on business needs and the platform being used, though most of the biggest platforms (like those listed above) support most, if not all, of the most commonly used programming languages and skills needed for AI.

TechRepublic covered in March 2018 the 10 most in-demand AI skills, which is an excellent summary of the types of training an organization should look at when building or expanding a business AI team:

    Machine learning
    Python
    R
    Data science
    Hadoop
    Big data
    Java
    Data mining
    Spark
    SAS

Many business AI platforms offer training courses in the specifics of running their architecture and the programming languages needed to develop more AI tools. Businesses that are serious about AI should plan to either hire new employees or give existing ones the time and resources necessary to train in the skills needed to make AI projects succeed.

How can businesses start using artificial intelligence?

Getting started with business AI isn't as easy as simply spending money on an AI platform provider and spinning up some pre-built models and algorithms. There's a lot that goes into successfully adding AI to an organization.

At the heart of it all is good project planning. Adding artificial intelligence to a business, no matter how it will be used, is just like any business transformation initiative. Here is an outline of just one way to approach getting started with business AI.

    Determine your AI objective. Figure out how AI can be used in your organization and to what end. By focusing on a narrower implementation with a specific goal, you can better allocate resources.

    Identify what needs to happen to get there. Once you know where you want to be, you can figure out where you are and how to make the journey. This could include starting to sort existing data, gathering new data, hiring talent, and other pre-project steps.

    Build a team. With an end goal in sight and a plan to get there, it's time to assemble the best team to make it happen. This can include current employees, but don't be afraid to go outside the organization to find the most qualified people. Also, be sure to allow existing staff to train so they have the opportunity to contribute to the project.

    Choose an AI platform. Some AI platforms may be better suited to particular projects, but by and large they all offer similar products in order to compete with each other. Let your team give recommendations on which AI platform to choose--they're the experts who will be in the trenches.

    Begin implementation. With a goal, team, and platform, you're ready to start working in earnest. This won't be quick: AI machines need to be trained, testing on subsets of data has to be performed, and lots of tweaks will need to be made before a business AI is ready to hit the real world.

Londoners march against more police powers...

This is scary. A little bit here, some there, it is how they take your rights away. Baby steps. This is the blueprint to fascism in our time. We are witnessing the steps to full control by some invisible power. There are already existing laws which could have been used against XR and BLM, but the police were ordered to stand by and watch. That was a political choice, not a lack of legal restrictions.

They were ordered to stand down by the Democrats to make Trump look bad on leadership, and saying he is a racist.

The government argues that the legislation is aimed at curtailing actions that cause "unjustifiable disruption or distress" to others", but with lockdowns and other convid restrictions  isn't that is exactly what the govt itself is doing, causing unjustifiable disruption and stress to others?

Zio oligarchy who rules the West is worried the sheep population may wake up and realise that they are nothing but slaves. So it is trying to put them in a cage before they can threaten its power. Sadly, most sheep willingly obey.

Resistance will become the new normal as people see that the pandemic was a cover story for a totalitarian government, who imposed martial law, limited the right of free speech and assembly, in a complete takeover of the civil space.

More of this needs to happen worldwide with all the attempts  going on by various local and country governments in their efforts to continue to strip the people of their rights.....their freedoms....keeping them under control and subjective.  The world goal is to make the people simply passive slaves controlled by whatever whim their government may impose upon them.

Resistance to the 'new normal' is happening everywhere the Dictatorships are attempting to keep their 'covid' powers.

‘Kill the Bill’: Londoners march against proposed law that would give police more powers to crack down on public protests
RT : 3 Apr, 2021

Many in the UK defied the rules and rallied against controversial legislation that would allow authorities to place additional restrictions on public protests there. London police had urged people not to attend the demonstrations.

The protests were held in 25 cities across England and Wales, including London. People marched from the capital's Hyde Park with placards saying 'Protect our rights' and 'Kill the Bill,' referring to the controversial proposed legislation, known as the Police, Crime, Sentencing and Courts Bill.

London's Metropolitan Police urged the public not to attend large gatherings that violate Covid-19 regulations and are punishable by fines. Similar rallies last month led to clashes with police and to arrests.

The bill, which passed its second reading in the House of Commons on March 16, gives police additional powers to restrict and control all public protests, while reinforcing the punishment for public disturbance.

The government argues that the legislation is aimed at curtailing actions that cause "unjustifiable disruption or distress" to others. The bill was introduced on the heels of the Extinction Rebellion and Black Lives Matter protests that also involved rioting and property damage.

Opponents of the bill believe it will allow a crackdown on the freedom of assembly. Labour Party MP David Lammy described the bill as "draconian" and argued that it will incentivize authorities to move against any protests they do not like.

Protests in the UK over proposed legislation that would crack down on public demonstrations have led to ten officers being injured and dozens people arrested, according to police, who describe the event as a “policing operation.”

“Today’s policing operation is still ongoing and arrest numbers may rise, but at this time, 26 people have been arrested for a variety of offences,” London’s Metropolitan Police announced on Saturday hours after numerous protests kicked off in 25 cities in the UK, including London.

It was also revealed that 10 officers have sustained injuries “during the operation,” but none of them have been serious.

In a statement, Commander Ade Adelekan, who is leading the “policing operation,” said the “vast majority” of protesters have adhered to social distancing guidelines “engaged with my officers when required and left when asked.”

A small majority being less cooperative, however, led police to move to “the enforcement stage,” according to Adelekan.

He added that the protests threatened the “progress” that has been made in the fight against Covid-19, and blasted “the selfish actions of a small number of people.”

"We remain in the middle of a global pandemic and we have made great progress in controlling the spread of the virus; we will not allow the selfish actions of a small number of people to put Londoners progress in jeopardy."

Just before their update, police urged people to “leave and return home.” Officials had previously warned citizens that such large demonstrations violate current Covid-19 restrictions and they could be fined by police.

Demonstrators on Saturday marched and gave speeches while holding signs with slogans such as ‘Kill the Bill,’ referring to the Police, Crime, Sentencing and Courts Bill. The proposed legislation would give police more authority in restricting public

protests, supposedly in an effort to avoid rioting and destruction to property. Critics believe the “draconian” bill is thinly-veiled crackdown on the freedom of assembly, which has already been de-facto heavily restricted by the tough British Covid-19 regulations.

Secretive Israeli nuclear facility confirmed by satellite!

In Dimona, a small city nestled in the Negev desert of southern Israel, is a nuclear plant first built in secret in the 1950s.

An obscure US regulation known as the Kyl-Bingaman Amendment compelled the US government to blur satellite imagery exclusively over Israel and the Palestinian Territories.

= = =

Clearer view of secretive Israeli nuclear facility is visible by satellite thanks to two archaeologists
By Tom Joyner in Jerusalem
ABC, Apr 5, 2021

It was late one evening in 2017 when somewhere in the minds of Michael Fradley and his colleague Andrea Zerbini a light flickered on.

The two Oxford University archaeologists often found they worked better after hours, once the office had emptied and with no phone calls or emails to distract them.

Both specialists in the Middle East, the pair were two years into a project involving satellite imagery of Israel and the West Bank when they noticed something odd.

"From the start, we knew that there was a problem," Dr Fradley said.

They were trying to access photos taken over Israel but noticed they were all blurry, and not high enough quality to make out any of the details on the ground they were looking for.

The blurry satellite imagery of Israel

They didn't know it then, but Dr Fradley and Dr Zerbini had run into an obscure US regulation known as the Kyl-Bingaman Amendment, which compelled the US government to blur satellite imagery exclusively over Israel and the Palestinian Territories.

During the Cold War, the United States' satellite program was as much a political instrument as it was a practical one. And at the time, lobbying of the US Congress by Israel, which argues allowing open access to satellite pictures endangers its national security, led to the passing of the little-known amendment at the end of the millennium.

With many of the world's largest commercially available satellite systems having long been owned by American companies, this has meant for decades satellite imagery over Israel on websites like Google Earth has been blurred out.

To the minds of the two researchers, there didn't seem to be a whole lot of information about the problem on the internet, and the situation presented an interesting challenge.

So, over many evenings in 2017, the pair got to work as they tried to find a workaround to accessing the detailed satellite imagery needed for their archeological research.

Working from their modest office on campus, they trawled what felt like every corner of the web, skimming through reams of declassified reports and endless newspaper clippings.

Suddenly Dr Fradley and Dr Zerbini found themselves in territory that was at once familiar and unfamiliar — the work gave them the same thrill of the hunt for new knowledge.

But it also gave them a feeling they were part of something subversive. They had become quasi-internet sleuths looking for answers on a subject that was very different to their day jobs.

"We were archaeologists," Dr Fradley laughed.

"We didn't really know anything about space law."

Finally, after two weeks of head-scratching, the pair had a breakthrough.

A French loophole to the rescue

The solution at first seemed so obvious that Dr Fradley's immediate thought was that they had made a mistake.

The pair had discovered there were other companies producing satellite imagery, at a cost. That included Airbus, a French aerospace company, which was among the first non-American companies to produce high-resolution satellite imagery of Israel and the Palestinian Territories, unbounded by the Kyl-Bingaman Amendment.

Having obtained clearer satellite pictures, Dr Fradley and Dr Zerbini set about publishing their findings, and wrote to the National Oceanic and Atmospheric Administration in the US, the agency that regulates satellite systems, to alert them to their discovery.

Under the Kyl-Bingaman Amendment, Israeli satellite imagery must specifically be "no more detailed or precise than satellite imagery of Israel that is available from commercial sources".

In July 2020, after some back and forth, the Kyl-Bingaman Amendment was quietly updated to remove its exception for Israel.

In response, Israeli Defence Ministry's head of space programs told an Israeli radio station that "we would always prefer to be photographed at the lowest resolution possible".

"It's always preferable to be seen blurred, rather than precisely," Amnon Harari said in July.

The rule change opened up brand new opportunities for scientific and environmental research that had previously been impossible.

But it also led to some unexpected discoveries.

The mystery of the nuclear facility

In Dimona, a small city nestled in the Negev desert of southern Israel, is a nuclear plant first built in secret in the 1950s.

Israel maintains a policy of what is known as "nuclear ambiguity", meaning it neither confirms nor denies the existence of a nuclear program, and it is not a signatory of the Nuclear non-Proliferation Treaty.

The airspace above the facility is closed to passing aircraft.

But if a plane were to fly over, its passengers would see below them a collection of squat buildings lining a grid of roads, all surrounded by sand.

For decades, that layout has barely changed.

But in February an international group of nuclear experts noticed "significant new construction" from analysis of commercially available satellite imagery

That imagery was made recently available by the relaxing of the Kyl-Bingaman Amendment.

Shortly afterwards, high-resolution photos of the Dimona facility produced by American company Planet Labs — only available since the relaxing of the Kyl-Bingaman Amendment — was obtained by the Associated Press, revealing a new dig about the size of a soccer pitch.

"[What] the Israeli government is doing at this secret nuclear weapons plant is something for the Israeli government to come clean about," Daryl Kimball of the US-based Arms Control Association told the Associated Press at the time.

Andrea Zerbini never saw the fruits of his labour

Today, Dr Fradley is still an archaeologist at Oxford University and looks back on the satellite imagery saga he helped shape with fondness.

"It was a really happy moment," he said, recalling the day the US government told him the rules were changing.

"But it was tinged with real sadness."

In 2018, his research partner and colleague Andrea Zerbini died after a battle with a rare form of liver cancer.

"He was the only other person who really kind of understood the nuances and how much we put into it," Dr Fradley said.

In an obituary for Dr Zerbini, Dr Fradley paid tribute to his friend and colleague.

"It is our work in early 2017 investigating an obscure piece of US [satellite] legislation that really sticks in my mind when I think of our time working together," he wrote.

"We were immensely proud of the work that we had done."