YouTube’s testing of a scheme to stop ad blocker usage and limit video plays is being seen by many as a way to further monetise the platform.

How Does YouTube Make Its Money? 

Google’s YouTube platform currently makes its money in several ways, with advertising being the main source of revenue. For example, its money comes from:

– Advertising. As mentioned, this is the primary source of YouTube’s revenue. Under this broad term, money is made from different types of ads, such as:

– – Display Ads. These appear to the right of the video and above the video suggestions list.

– – Overlay Ads. These are semi-transparent ads covering approximately 20 per cent of the video content at the bottom.

– – Skippable Video Ads. The adverts that allow viewers to skip ads after 5 seconds. Advertisers are charged only if the viewer watches the ad for at least 30 seconds or until the end.

– – Non-skippable Video Ads. The adverts users must watch the whole of before the video starts, plus they can also appear during, or after the main video.

– – Bumper Ads. The non-skippable video ads of up to 6 seconds that viewers must watch before the video.

– – Sponsored Cards. These ads are supposed to display content that may be relevant to the video, e.g. the products featured in the video.

YouTube Premium. This is the YouTube subscription service whereby subscribers pay a monthly fee to watch ad-free content, gain access to the YouTube Original series, and it also includes a subscription to YouTube Music.

Channel Memberships. Any YouTube content creators with a large enough following can offer their subscribers a membership with monthly payments in exchange for badges, emojis, and other perks.

Super Chats & Super Stickers. During live streams, viewers can pay money to pin their comment at the top of the chat. This gives it more visibility and allows creators to easily acknowledge or respond to them.

The YouTube Merch Shelf. Popular channels can showcase their official merchandise on YouTube itself, i.e. creators can display the merchandise below their videos.

Transaction Revenues. YouTube also makes money by renting or selling movies and TV shows.

What’s Happening With The Current “Experiment”? 

As first highlighted by a Reddit user, YouTube is running an experiment (globally). As a result, users who have an ad blocker on when they view YouTube are shown a pop-up that notifying them that “video player will be blocked after 3 videos”. 

Below this main message is a three-part statement which says:

“It looks like you may be using an ad blocker. Video playback will be blocked unless YouTube is allowlisted or the ad blocker is disabled. 

Ads allow YouTube to stay free for billions of users worldwide. 

You can go ad-free with YouTube Premium, and creators can still get paid from your subscription”. 

There have also been reports (in June) of YouTube restricting ad blockers on mobiles.

This latest experiment follows one appearing during May when YouTube was stopping users with ad blockers on from viewing the platform.

Why? 

As shown by the message, it appears that YouTube may be trying to further monetise its platform by pushing users towards signing up to its YouTube Premium subscription service.

Over the last year or so, YouTube has made several moves aimed at this. For example:

– In October 2022, YouTube ran a short-lived experiment where it asked some free users to upgrade to Premium to watch videos in 4K resolution. The test was reported to have attracted criticism and ended with YouTube saying that all users would be able to access 4K quality resolutions without Premium membership.

– In September 2022, YouTube ran another test aimed at encouraging users to sign up to Premium to avoid adverts by playing 11 unskippable ads before a video began.

– Back in July 2021, some may remember that YouTube tested a feature that allowed users to directly purchase products from YouTube that were featured in the video they were watching. At the time, it was reported that YouTube had been asking creators to tag and track the products used in their videos so that the data could be sent to Google. This may have been used to help improve its analytics, to develop shopping tools for YouTube, and possibly (as was thought at the time) to contribute to a future integration with Shopify (a competitor to Amazon).

YouTube Premium 

This time last year, Google’s announced that its ad-free service (with access to exclusive content and other perks) YouTube Premium had 80 million YouTube Music and Premium subscribers globally (including customers with free trials). As a platform, YouTube is vast and caters to a wide array of content and audiences, and its diversity allows it to be relevant across various domains and demographics. That said, YouTube Premium is in competition with several platforms across various domains. Competitors include:

– Streaming services like Netflix, and Amazon Prime Video (YouTube Premium’s “Originals” content is in direct competition with these), and Disney+.

– Music streaming platforms like Spotify, Apple Music, Tidal, Deezer, and others.

– Ad-free video platforms like Vimeo.

– Short-form content platforms such as TikTok, Instagram (Reels), and Snapchat.

– Live streaming platforms like Twitch, which is primarily gaming content-focused but still competes for user attention in video streaming.

What Does This Mean For Your Business? 

The many experiments in recent times involving matters relating to adverts and ad blockers appear to be aimed at getting more YouTube users to sign up to the YouTube Premium ad-free subscription service, thereby bringing in more revenue from this well-established video sharing and social media platform.

YouTube is already the largest video sharing platform (1.9 billion active logged-in users monthly) and wants to not only stay that way, but to boost its opportunities for revenue, e.g. the experiment to buy goods featured in videos, driving users to Premium sign-ups, possible integration with other platforms (Shopify), and more. YouTube’s relatively diverse services (within video and music) plus its status as a social media platform and general focus of user attention means that it now has many powerful competitors in the streaming and content platform worlds from Netflix to TikTok. This in itself is an incentive to find opportunities to stay ahead and search for new opportunities and leverage the power of the platform and the strength of its parent company. That said, people are used to using YouTube for free, are frustrated by adverts, and tend to respond poorly to feeling like they are being forced or bored (by long adverts) into signing up to yet another subscription, particularly with a cost-of-living crisis.

Another option that YouTube could consider is a paywall, although this may need a bit of thinking-about and could alienate and even lose them many non-paying but still frequent users, not to mention the bad publicity. From all the signs given by the experiments, more pushes to encourage Premium sign-ups are the likely course for now so users should expect more ad blocker / ad-related issues in the near future.

Researchers at Carnegie Mellon University have reported finding a simple way to exploit a weakness and disrupt major chatbots like ChatGPT, Bard, and others.

Incantation 

The researchers discovered that if they add specifically chosen sequences of characters (an incantation) to a user query, it causes the Large Language Model (LLM) system to obey user commands, even if it produces harmful content.

Works On Many Different Chatbots 

The researchers say that because these types of adversarial attacks on LLMs are built in an “entirely automated” fashion, this could allow someone to create a virtually “unlimited” number of such attacks. Adversarial attacks refers to the method of altering the prompt given to a bot so as to gradually move it toward breaking its shackles and ‘misbehaving’.

Although the researchers built their attacks to target open source LLMs in their experiments, they discovered that using this method of adding strings of specific characters to queries works for many closed-source, publicly available chatbots like ChatGPT, Bard and Claude.

Security Challenge 

The discovery of this particular weakness raises some serious concerns about the safety and security of popular Large Language Models (LLMs), especially as they start to be used in more autonomous fashion.

It May Not Be Possible To Patch 

The researchers have said what is most concerning is that it’s not clear at this point whether LLM providers will be able to patch this vulnerability, adding that “analogous adversarial attacks have proven to be an exceedingly difficult problem to address in computer vision for the past 10 years”. 

Also, the researchers believe that the very nature of deep learning models makes these kinds of threats inevitable and have suggested that these considerations should be taken into account as we increase usage of and rely more upon AI models in our lives.

What Does This Mean For Your Business? 

The threats posed by AI have been highlighted a lot lately, not least by the open letter signed by many tech (and AI) leaders calling for six-month moratorium on the training of AI systems more powerful than GPT-4 to mitigate AI’s risks to society and humanity.

Discovering a vulnerability, therefore, that appears relatively easy to exploit (which it may not be possible to patch) raises serious security concerns, especially with more businesses becoming more reliant on AI chatbots like ChatGPT, Copilot, and more. With generative AI being a very helpful yet a very new tool for businesses (ChatGPT was only released in November) and given the nature of LLMs, it’s probably to be expected that there are bugs and possible zero-day issues yet to be discovered. Also, as the researchers pointed out, methods like analogous adversarial attacks have been tough to defend against for a decade.

All this means that businesses may be more exposed to risk than they would like but need to weigh up the benefits against the risks (researchers often discover things which aren’t actually being exploited yet in the real world) and hope that advances in AI chatbots are very soon accompanied by advancing security levels.

Following its growth during the pandemic, video communications company Zoom, which made remote working possible for many, has now ordered staff back to the office for two days a week.

Those Within A Commutable Distance 

This move from Zoom, which many see as a trend among big companies to pull-back from their major commitments to flexible working, will require workers within a “commutable distance” (i.e. 50 miles / 80km) of the company’s offices to come to the office twice a week for designated team days.

Not Many In The UK 

It is not known exactly how many of Zoom’s 8,400 (mostly US-based) workers will be affected by the new policy from the Californian company. Few of those affected, however, are likely to be in the UK because only around 200 people work for Zoom’s newly opened London office.

The rollout of the change to work practices will take place through this month and September on a staggered timeline depending on which country staff are in.

Structured Hybrid Working 

The decision by the company is reported to follow its move towards a “structured hybrid approach” to work. This contrasts with Zoom saying, at one time, that all staff could work remotely indefinitely, but the move is not unexpected. For example, 29 per cent of US workers now operate within hybrid policies (Stanford).

Zoom is one of the last of a number of large corporations to order a return to the office to some extent, perhaps lagging because of its close association with the idea of working remotely (which its product enables), and it has faced tough competition in recent times.

Will Hire Regardless Of Location 

That said, Zoom has said that it still intends to “hire the best talent, regardless of location”.  

Trouble At Mill In Recent Years 

Whereas Zoom enjoyed a massive boost in user numbers during the height of the pandemic, e.g. 10 million daily users in December 2019 rising to a staggering 300 million less than six months later, these days it’s a different story. For example, Zoom has suffered in recent times from:

– Falling net profits.

– Strong competition and improved, expanded offerings from remote work platform rivals like Microsoft Teams and Slack.

– A fall in share price, i.e. from $500 in October 2020 to $68 now (August 2023).

The company’s worsening fortunes prompted 1,300 jobs to be cut earlier this year.

What Does This Mean For Your Business? 

With the pandemic now appearing to be quite a long way in the rear-view mirror, even though the value of remote working is known, it is no longer so high upon the priority list with many big companies rowing back and finding a happy medium with hybrid working.

The move by Zoom has only really gained clout as news story because its business has been to enable remote working (the irony) but, with new post pandemic priorities in the marketplace, serious competition and improved offerings from Teams and Slack, falling popularity and share prices, and with AI being the new popular kid on the block overshadowing all else, Zoom’s move back to the office may be necessary.

Zoom has itself just launched a new product, the AI-powered Intelligent Director, which “brings together in-office and remote employees,” thereby accepting and marketing for the changing times and fighting back by introducing AI.

In this insight, focusing particularly on AI and ML, we look at what synthetic data is, where it comes from and what it’s used for, its challenges, and the implications for businesses.

What Is Synthetic Data? 

Synthetic data is data that’s been artificially created rather than derived from real-world activities. Synthetic data is not a new idea and had its first major commercial use in developing simulations for autonomous vehicle systems. Today it’s become particularly useful in Artificial Intelligence (AI) to train Machine Learning algorithms.

Why Use Synthetic Data? 

The training and development of the ML algorithms behind AI requires access to substantial amounts of the right kind of data (datasets). With this in mind, there are several good reasons why synthetic data rather than real-world data is used in training AI models.  For example:

– Generating and using synthetic data is often less costly and time-consuming than collecting real-world data, thereby leading to cost and time efficiency. Using synthetic data can also save costs and time, for example in providing image data, because no manual data labelling is required.

– Synthetic data also provides a solution to privacy concerns because it doesn’t contain information about real individuals (data protection).

– In terms of bias mitigation, synthetic data can be constructed to represent a wide variety of information, thereby reducing biases in AI models.

– Using synthetic data can also effectively serve the need for quality control because it can be tailored to specific needs, including cases that might be hard to find in real-world data.

– Synthetic data can be generated on demand, in whatever volume is required, making it very practical, convenient, and efficient, plus a very fast way for businesses to acquire data.

– Using synthetic data allows the creation of machine learning models made for scenarios that wouldn’t have been possible before, say if the data either didn’t exist, wasn’t good enough or perhaps had restrictions on it.

– Using synthetic (rather than real-world) data is a way to avoid the challenges posed by restrictions on data in some sectors such as healthcare and finance.

Where Does Synthetic Data Come From? / How Is It Created? 

Synthetic data is generated using several key techniques. For example: |

– Simulations and 3D models provide a means to create imagery and objects. This is particularly useful for training image classifiers without having to use real-world data.

– Data augmentation involves taking existing real-world data and applying various transformations to create new data points that retain the underlying patterns and information of the original dataset. For example, if the original data consists of images, data augmentation might include rotating, flipping, or cropping these images to create new variations. These alterations expand the dataset, allowing models to learn from a broader array of examples without the need to collect additional real-world data.

– Generative models, such as Generative Adversarial Networks (GANs), transform datasets while preserving essential characteristics, without exposing sensitive information.

– Diffusion models, which are used in image generators like DALLE-2 can use ‘denoising’ technology to create synthetic data from random inputs.

– Neural radiance fields (NeRFs) can produce synthetic data by manipulating parts of the process of turning two-dimensional images into three-dimensional scenes.

Together, these methods offer a multifaceted approach to generating synthetic data, facilitating a wide range of applications in various fields.

What Are The Drawbacks And Challenges Of Using Synthetic Data? 

Although it’s generally a powerful and cost saving tool, generating and using synthetic data does have several challenges and drawbacks. These include:

– Complexity. Crafting high-quality synthetic data is not trivial and needs not only specialised skills but also profound understanding of the underlying domain. This complexity can be a barrier to entry for smaller firms or projects lacking expert resources.

– Effectiveness compared to real-world data. The efficacy of synthetic data when compared to real-world data is still a subject of ongoing investigation. While it can mimic real data, some question whether it really can fully represent the nuanced variations found in genuine datasets.

– Quality and Bias. Incorrectly constructed synthetic data can inadvertently lead to biased or flawed conclusions. Ensuring that synthetic data accurately represents diverse scenarios without incorporating biases is, in itself a nuanced and complex task. If this balance isn’t right, models trained on this data might perform poorly in real-world applications.

– A privacy trade-off. Generating synthetic data often stems from a need to circumvent privacy issues inherent in using real-world data. However, there is an intricate balance between the usefulness of the synthetic data and the level of privacy it offers. If the synthetic data is too detached from the original, it may lose valuable insights. If it’s too closely related, it might still expose sensitive information.

– Regulatory considerations. As previously mentioned, in sectors like healthcare and finance, where data handling is heavily regulated, the use of synthetic data must still comply with existing laws and guidelines. Ensuring this compliance while maintaining the data’s utility can be a complex and time-consuming process.

What Does This Mean For Your Business? 

The ability to generate data on demand in whatever quantity is required, tailored to specific needs which may not be available in the real world, and free of restrictions, bias and other complications is driving the AI development that’s bringing new opportunities and solutions in many sectors.  For example, for research and healthcare, having cost-effective access to on-tap specific datasets is speeding up training for and scaling models so that they can solve problems faster and explore and make breakthroughs in new areas with new simulations and scenarios and innovative algorithms in a way they couldn’t before, due to the restrictions and limitations of real world data. The disruptive force of synthetic data is changing the whole economy and strategy of data in way that is benefitting the creation of better and more specific AI models in a variety of industries and creating new business opportunities for startups along the way.

As highlighted by Gartner’s prediction that 60 per cent of all data used in the development of AI will be synthetic rather than real by 2024, synthetic data is rapidly becoming the preferred choice in the AI world for a wide range of reasons, and its use is speeding up and helping to deliver new and beneficial possibilities for businesses and individuals.

International Airlines Group (IAG) has announced another “significant” investment in Nova Pangaea Technologies (NPT), a company that makes aviation fuel from non-food agricultural waste and wood residues.

IAG & NPT 

International Airlines Group (IAG) is the parent company of Aer Lingus, British Airways, Iberia, Vueling and LEVEL, and Nova Pangaea Technologies (NPT) is a Teesside-based cleantech company whose biofuel-making technology offers IAG a pathway to the production of Sustainable Aviation Fuel (SAF).

How Does NPT Make Aviation Fuel From Agricultural Waste And Wood Residue? 

At the moment, NPT’s technology can convert agricultural waste and wood residue feedstocks into second-generation bioethanol. This could be processed into fuel if NPT had the right production facility, which the investment from IAG will provide.

The process that NPT uses to turn waste and wood residue feedstocks into second-generation bioethanol using its REFNOVA® branded environmentally friendly process.

The process involves pouring agricultural waste like particle size reduced feedstock into a fluidisation vessel to neutralise alkali earth metals, then using a hot air dryer to remove excess moisture, before using high temperatures to extract the lignin component and vapourise the sugars. The vapourised sugars are then condensed into NOVASUGARS which can be fermented into bioethanol.

Investment Will Build A New Specialised Aviation Fuel Plant 

In addition to its $865 million commitment to SAF, IAG’s latest investment will progress the development of ‘NOVAONE’, NPTs first waste-to-fuel commercial-scale production facility. This will be the first of its kind in the UK and construction is expected to begin later this year, with the facility producing biofuels by 2025 (creating major employment opportunities in the North-East).

The Benefits 

For IAG, some of the main benefits of investing in NPT include:

– Securing a supply of SAF ahead of the introduction of the UK Government’s SAF mandate, which is expected to be introduced from 2025.

– Meeting its target of using target of one million tonnes of SAF by 2030. IAG was the first European airline group to commit to the use of 10 per cent SAF by 2030.

– Supporting the decarbonisation of its own and the other airlines in its group. IAG intends to be net zero by 2050.

Some of the main benefits of the widescale use of SAF in the aviation industry could be:

– Decarbonising by replacing the need for fossil fuel derived and synthetically created materials.

– Saving costs.

– Contributing towards a net-zero global economy.

– Maximising the use of unwanted and already farmed by-products rather than driving the need for more fossil fuels and contributing to climate change, i.e. minimising the impact of its consumption on the natural world.

– Having the ability to meet its own green targets and government green targets and mandates.

The Only Realistic Option For Long Haul Decarbonisation 

Luis Gallego, IAG’s CEO, said: “Sustainable Aviation Fuel is the only realistic option for long haul airlines to decarbonise, which is why investment in this area is so critical.” 

He also highlighted the commitment needed by saying “We are not just buying SAF, we are willing to invest in developing the industry, but we need governments in the UK and Europe to act now to encourage further investment.” 

A Transformational Milestone 

Sarah Ellerby, Chief Executive of Nova Pangaea Technologies, highlighted the importance of the investment by AIG in NPT and the new SAF plant, saying:

“This is a transformational milestone, and a real endorsement of the crucial work Nova Pangaea Technologies is doing” and that “Our facility will be the UK’s first commercial plant of its kind, and it will play a crucial role in decarbonising the aviation sector, as well as providing local employment opportunities”.

Electric Engines Still Some Way Off 

Aircraft using SAF is therefore probably the only good interim solution in the period between winding down on the use of fossil fuel engines and before the introduction of electric engines that are capable acting as suitable replacements, certainly for long haul.

The development of electric aircraft engines still has several significant challenges to overcome, e.g. issues like energy density, weight, charging infrastructure, regulation, certification, and economic factors are all substantial hurdles.

Smaller, short-range planes and urban air mobility solutions are making progress, with some small electric planes in operation and hybrid systems being explored.

However, the widescale introduction of fully electric engines in commercial long-haul aviation looks likely at least a couple of decades away. Battery technology needs to improve, and substantial investments in research, development, and infrastructure are required.

What Does This Mean For Your Organisation? 

With the aviation industry (a major fossil fuels customer and CO2 producer) needing to find an effective and sustainable way to decarbonise and still operate effectively, the fact that commercial electric engines are years away means something is urgently needed (particularity for long haul) in the meantime.

Government mandates and green targets are looming so it’s not surprising that IAG has been investing in an SAF producer (NPT) and has financed the building of a production plant. The SAF supply will give IAG the chance to start decarbonising its wider fleet as well as meeting its green targets, staying ahead of government mandates, and showing their green credentials and commitment.

The wide use of SAF would have many environmental benefits, e.g. maximising usage of natural waste products, reducing reliance on fossil fuels, cutting CO2 and emissions pollution, and more. With the input product being agricultural waste and a plants being built like the NPT one, this could make aviation fuel much more sustainable and could help in tackling the climate crisis before alternatives like clean electric commercial aircraft engines come along.