• Cycling has been shown to increase happiness in UK cities.
• Cycing in cities has been promoted and encouraged for years in the UK.
• There’s at least some evidence that cycling in cities, rather than driving to and from work, can boost people’s productivity

The UK has lived in what it would be fair to call “interesting times” since 2016. The year of Brexit (known elsewhere as the year of Trump) brought chaos, political stagnation, economic confusion, and eventually a mass exodus of workers from the European Union as a legal break from the union took effect.

That was followed by Covid, with soaring death rates as the government, as it transpired, broke its own lockdown rules and partied while people around the country could not attend funerals for the ones they’d loved and lost.

The Queen, who had been on the throne for over seventy years, passed away.

Rocketing fuel prices, partly as a result of Vladimir Putin’s illegal invasion of Ukraine and partly because of an ideological refusal by the government to rein in energy company profits, even when asked to do so by the energy companies themselves added economic terror to everyone under 10 and over 50 as food or heat became a genuine question in the winter, and the rise in food poverty meant there were for the first time in half a century more charity-run food banks in the UK than there were McDonald’s restaurants.

And during the course of the last seven years, the UK has had five Prime Ministers, without once changing the party in power. One of those Prime Ministers lasted just 44 days and crashed the economy, while another has since resigned from parliament altogether ahead of being found to have lied both to the nation and the House of Commons, knowingly, about the debacle of Covid parties and lockdown rule-breaking.

All of this is not intended to paint the UK as a burning dumpster-fire hellscape straight out of a Trump speech. It’s simply by way of illustrating that when you discover there’s a productivity crisis in UK cities, there should be no sense in which it comes as a surprise. It would in fact be remarkable if there were not a productivity crisis after a time like that. The country’s been through a lot – and so have its workers.

So, what solutions have been suggested to such a productivity crisis? Massive investment in jobs and infrastructure, with a pathway from education to rewarding, taxpaying, house-affording work?

Well, yes, but inflation makes that difficult, and besides, the UK government has its hands full with a supposed crisis on its sea-border with France right now.

So, then… what’s the solution to the productivity crisis in UK cities?

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Cycling.

No wait, come back. Recent research has revealed that cycling can, at least partially, tackle productivity slumps in UK cities.

Cycling in cities boosts happiness.

According to the research, the greater the number of cyclists you see on the roads in your region, the happier in general terms the region is likely to be.

Regions such as the South East and South West of England came out on top as being the happiest of all areas of Britain. Those areas have also been revealed to have higher life expectancies and less congestion on the roads. And one of the common factors between those regions is… a higher proportion of people cycling in the cities in those areas than elsewhere.

A coincidence, you say?

“One of us! One of us!” Would you like to be stuck in this?

Well, certainly, a healthy degree of scepticism regarding cause and effect pairs is always wise, but actually, it looks as though cycling beats the odds, especially when we take into account European cities that have high percentages of people committing to cycling to and from work, such as Amsterdam and Copenhagen (48% and 50% respectively), and a higher quality of life – Amsterdam is 6th and Copenhagen is 10th on the Quality of Life Index 2023.

London, meanwhile, lies a measly 65th on the list.

So, does the pursuit of happiness lead us to a bicycle? E-bike engineers Swytch Technology decided to find out with a first of its kind study to look at the link between cycling and happiness. And the results point to a categorical connection, with 15,356,000 Brits reporting that their mental health improves when walking or cycling for regular periods – which most people in cities would probably pay for if it came labelled as a therapy.

Cycling advocacy schemes in UK cities.

It’s no secret that the UK Government and local councils have tried to push cycling as the “go to” form of transport, particularly in cities, where road congestion is high, with Cycle to Work and Vision to Cycle schemes.

According to Swytch’s study, the schemes are working, with 6.3 million people saying that they plan to not own a car within the next five years. 4 million of those surveyed revealed that their preferred method of transport qualified as micromobility tools, including e-bikes and e-scooters.

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PRODUCTIVITY

Post-productivity employee value assessment and the KPI factor

Already, 3.8 million Brits now cycle or use an e-mode of transport to get to their place of work, a steady increase that continues each year. Over 9 million have reported that they no longer use a car to commute – and that can only grow as controversial schemes to charge motorists in outlying suburbs for driving into cities expand, as a way of tackling greenhouse gases and an urban air quality crisis. The British attitude to travel is certainly shifting from fifth gear back into more sustainable modes of transport.

Environmental fines are making driving in cities less attractive than it has been.

So, with more and more Brits jumping on their saddles and choosing to cycle as a way to commute, the question is – why do British towns and cities struggle to match the quality of life and mental health levels of their European counterparts? According to Swytch’s study, it could be down to those daily commutes from hell in the UK. The study found that 5.5 million Brits said their commutes were the most tiring part of their whole day.

Cycling in UK cities – honestly, less of a death-trap.

That might seem incredible to readers in the US, but it’s worth remembering that British cities have been evolving more or less since Roman times, and while there may be significantly less to cope with in terms of actual distance travelled, it is a trope that in the UK, chaos, carnage and bureaucracy will always expand to fit every inch of space provided.

When we consider that, in addition, 8.2 million Brits get queasy just thinking of using public transport despite the fact that it’s significantly more available there than it is in the US, it suggests that the route to better happiness levels and improved quality of life could be getting active and getting in a cycle lane.

Changes to make it easier to cycle in cities could boost productivity in the workforce.

An active commute, such as cycling, jogging, or walking, rather than a sedentary commute (cars, trains, buses), may be beneficial for both mental and physical health.

Being active may also have significant benefits to someone’s productivity. Swytch discovered that 4.83 million of those surveyed experienced a boost in productivity levels since they began cycling or walking to work. With a possible “productivity crisis” seizing the UK, a healthy change to daily commutes may be the answer to promoting improved productivity and happiness in the workplace.

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Yes, the Cycle to Work scheme was introduced to lower congestion and encourage better physical health, but it may have profound impacts on a person’s mental wellbeing, as well as creating a more energized, engaged workforce.

The question remains – would it work in Manhattan?

The revolution will be two-wheeled. Psychedelic sections will not be mandatory. Fun, but not mandatory.

The post Cycling to solve productivity crisis in UK cities? appeared first on TechHQ.

• Software publishers should adopt open-source for publicly-funded code.
• European pressure group’s camapign gathers momentum.

Many of the benefits our taxes pay for are for everyone, including ourselves, to use. Roads, street lighting, police forces, public transport, and a thousand more everyday necessities are provided by governments and funded by the direct and indirect taxes we all pay every day.

So why should software and software publishers be different? That’s the question posed by the Free Software Foundation Europe in its Public Money? Public Code! campaign that it’s running right across Europe.

The thought behind the campaign is very simple: if taxpayers pay for software commissioned by governmental bodies, that software should be open for everyone to use.

At a time when public budgets are tight and contracting, public institutions are positively encouraged to share resources, minimize expenditure and not duplicate existing infrastructure.

It seems odd, therefore, that one local authority can commission software from the private sector, for example, but should a neighboring authority want similar services, they too have to spend their citizen’s data to get a similar, if not identical, outcome.

The FSFE’s campaign is pushing for software developed on the taxpayer’s account to be open to all to copy, redistribute and re-use. By procuring software that is released under open-source licenses, all taxpayers benefit.

Source: FSFE

Why open code, ask software publishers

Open-sourcing public software projects massively lowers the costs to the taxpayer. Instead of five municipalities each paying for the development of, for example, a smart public transport timetabling platform, it could be commissioned once and then shared. Individual municipalities could, if they wanted, pay a little more for the code to be right-sized for their specific use cases, but essentially, software procurement costs fall for everyone.

Currently, the ethos of resource-sharing and cost consciousness seems to stop at the point of procurement, where multiple public sector bodies all have to pay the private sector piper.

In the public sector’s IT space, many look to the cloud to operate more efficient and cost-conscious services to the citizen. SaaS provisions from software publishers, it is said, provides ready-made solutions to many of the needs of the public sector. Using these services is cheaper than developing from scratch and cheaper than paying in-house developers and hardware costs for on-premise solutions. But taxpayers’ money ends up paying large private sector companies multiple times. The cloud may offer some savings over legacy solutions, but such savings are pitiful compared to those available if the FSFE’s campaign were to gain more traction.

The arguments against open-source in this context tend to focus on job creation and the positive financial effects of the public sector commissioning software that delivers services. However, there are no local positives for a local government or town council: money flows to large municipalities where the big tech companies have offices, or worse, overseas.

Source: FSFE via Mastodon.

Civil society organizations, developers, technologists, and activists across the continent have signed an Open Letter to attempt to convince local and national lawmakers of the savings and benefits of public code. As is often the case with FOSS and technology above a certain level, the FSFE’s role is partly educative in this campaign. It’s easy to equate something “free” with something of no value.

Defining open-source

But “free” in this context, and the FSFE’s title, refers to “free” as in “freedom to see, redistribute, copy and improve.” Once that distinction is made, the economic arguments for Public Money? Public Code! are unassailable.

The public sector is one of the big purchasers of IT and software today from hardware vendors and software publishers, with up to 27% of software companies’ revenue coming from the sector. That type of financial weight can change practices locally and nationally, delivering huge savings for citizens who, like their governments at all levels, are experiencing tough economic times.

This campaign makes sense to any technologist aware of the transformative nature of open-source software. It must make sense to accountants in your local city hall, too.

The post The public-sector’s software vacuum appeared first on TechHQ.

This week, esteemed economists and bond managers have warned that there is still potential for an economic downturn this year. Interest rates have been hiked up to over 5%, helping to combat inflation, but also pushing a decrease in consumer spending and making borrowing more costly.

Therefore, many employees likely feel some degree of financial stress. According to a survey by Northwestern Mutual, about three-quarters of American adults expect a recession to have a high or moderate impact on their finances in both the short and long term.

This pressure can take a toll on their ability to perform their best at work. It manifests as on-the-job mistakes or accidents, low morale, and diminished productivity, and can extend beyond the individual employee. When one team member is experiencing financial difficulties, co-workers may need to pick up their slack or assist with additional tasks, which can strain team dynamics and lead to resentment or burnout. Additionally, when employees are preoccupied with their personal financial challenges, they may have less energy and enthusiasm to contribute to collaborative projects, brainstorming sessions, or creative problem-solving initiatives.

But the most significant impacts of financial stress on an employee’s ability to work come during times of immediate financial crisis. These could be unexpected medical expenses, car repairs, or home emergencies, resulting in decreased concentration or even sudden absence. Without an emergency savings solution in place, these events may force them to take out a 401k or payday loan, work a second job, or get into credit card debt. In the long term, it can lead to delayed retirement and a diminished quality of life.

Source: iStock

It is an employer’s responsibility to support their employees during these challenging times and provide avenues for financial relief and assistance. However, many struggle to implement effective initiatives that yield tangible and measurable results.

One way of helping to keep your workforce secure during a financial emergency is to offer a workplace Emergency Savings Account (ESA). These are dedicated funds set aside specifically for unexpected expenses and act as a financial safety net. Individuals can dip into this pot to weather emergencies without resorting to high-interest loans, depleting retirement savings, or experiencing undue stress.

What’s more, providing your employees with an ESA demonstrates your commitment to their overall well-being and helps them build a strong foundation for financial resilience. Employers can offer incentives, like matching a portion of their ESA contribution with each paycheck they receive. They could also offer a sign-up bonus or provide an additional match when they reach a certain milestone.

The new SECURE 2.0 Act means that, from 2024, employers can offer their staff an ESA as part of their retirement plan. The employee could elect to have up to 3% of their pay automatically placed in their in-plan ESA and then be able to make at least four withdrawals a year which aren’t subject to fees. However, after-tax contributions to an in-plan ESA are capped at $2,500.

In-plan ESAs can also incur fees and additional work for the employer due to the necessary ERISA compliance and other support costs. They also require the company to offer a retirement savings plan in the first place, which can be challenging for those lacking the resources or expertise to establish and maintain such plans.

Source: iStock

Alternatively, out-of-plan ESAs are protected from early withdrawal penalties or tax implications. The majority of these accounts do not have caps on contributions, and all the money is available as soon as it is required. The provider will also ensure that they are compliant, shifting the responsibility away from the company’s HR and finance divisions. Employees have the added benefit of being able to easily withdraw their contributions even if they change employers. Most out-of-plan ESAs are insured by the Federal Deposit Insurance Corporation (FDIC) and, from certain providers, can even accrue interest.

One prominent provider of out-of-plan ESAs is SecureSave. The company boasts a simple step-by-step setup process and an easy-to-use platform for HR teams to manage ESAs.

SecureSave ESAs are accompanied by a mobile and web app that allows employees to quickly see how much they have saved and all their benefits, like employer contribution matching and incentive programs. This has resulted in an average participation rate of 56% in companies that have adopted the ESAs.

SecureSave says that the average user has $1,000 saved after one year and on average, 89% of all emergency funds stay in the account. This helps secure every employee’s financial well-being and fosters confidence in the workplace. It is the first financial wellness product that has shown it can cultivate consistent employee savings habits at a large scale.

To learn more about setting up ESAs for your employees, contact SecureSave today.

The post Emergency Savings Accounts: Why they benefit both employers and staff appeared first on TechHQ.

Graphics processing units (GPUs) may have their origins in enriching the display of arcade game visuals, but today their parallel processing capabilities are in demand across a wide range of business applications. The popularity of OpenAI’s ChatGPT is driving large language model (LLM) integrations that could radically transform sectors such as the provision of legal services (Goldman Sachs estimates that more than two-fifths of legal tasks could be carried out by AI). And this race to capitalize on generative AI, which requires significant computing resources, has got developers thinking about various IaaS models, including so-called Airbnb for GPUs.

What is Airbnb for GPUs?

Cloud computing providers such as AWS, Microsoft Azure, Google Cloud Platform, Alibaba Cloud, IBM Cloud, and other major IaaS vendors make GPUs available for users to train, fine-tune, and apply generative AI services. But the charges can soon add up for customers. OpenAI’s GPT-4, its most advanced commercially available LLM, can handle more than 25,000 words – generating time-saving summaries of long business documents. But these tasks are computationally expensive, given the complexity of the latest AI models.

Putting idle silicon to use: text-to-image generative AI image created using algorithms running on distributed computing.

However, just because services provided by major cloud computing vendors have become the common way for users to access large-scale GPU infrastructure doesn’t mean that other options don’t exist. And one of the most interesting alternative IaaS models is a decentralized computing approach dubbed Airbnb for GPUs.

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In the same way that staying in online rental property gives tourists an alternative to using hotels, Airbnb for GPUs makes idle hardware available for use through distributed computing approaches, often at a fraction of the cost of services offered by major cloud computing providers.

To picture the scene, it’s useful to think of successful citizen science initiatives such as Folding@home. Citizens volunteer computing time on their personal machines to run simulations of protein dynamics that help researchers to understand the behavior of diseases and develop treatments. Back in 2007, Sony updated its PlayStation software to enable console owners to participate. And over the next five years, more than 15 million PlayStation 3 users donated over 100 million computation hours.

“The PS3 system was a game changer for Folding@home, as it opened the door for new methods and new processors, eventually also leading to the use of GPUs,” commented Vijay Pande, Folding@home research lead at Stanford University on Sony’s PlayStation blog. The success of Folding@home and other distributed computing projects that pooled hardware, which was otherwise just sitting idle as owners were out at school, work or sleeping, has inspired a variety of cloud alternatives based on an Airbnb for GPUs type model.

One of those is Q Blocks, which was founded in 2020 by Gaurav Vij and his brother Saurabh. The firm uses peer-to-peer technology to give customers access to crowd-sourced supercomputers. And the company enables owners of gaming PCs, bitcoin mining rigs, and other networked GPU hardware to make money from idle processers by registering as hosts.

Systems need to be running Ubuntu 18.04, have a minimum of 16 GB of system RAM and 250 GB of free storage per GPU, and various other prerequisites, including that GPUs should be maintained 24/7. And by sharing these resources, users can help machine learning developers to build applications at a much lower cost.

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Gaurav Vij had witnessed firsthand how quickly AWS bills can add up, having bootstrapped a computer vision startup. And his brother, who had worked as a particle physicist at CERN – another beneficiary of volunteer computing through its LHC@home program – recommended that they team up to build an Airbnb for GPUs. Using Q Blocks distributed computing services, Vij’s startup development costs were reduced by 90%.

More recently, the Q Blocks team has added a generative AI platform to its crowdsourced GPU offering dubbed Monster API, which gives users affordable access to a wide range of generative AI models. Signing up to Monster API allows users to explore Stable Diffusion’s text-to-image and image-to-image capabilities. Other generative AI services include API access to Falcon 7B – an open-sourced alternative to ChatGPT.

Generative AI text-to-speech: Bark examples

Monster API also lets users carry out text-based image editing via InstructPix2Pix, transcribe speech using Whisper, and convert text to an audio file using the Suno AI Bark model. Just considering the opportunities of text-to-speech, which has been described as ‘the ultimate audio generation model’, there are loads of business use cases to explore. For example, generative AI text-to-speech makes it easy to produce multilingual content, music, background noises, sound effects, and non-verbal communications.

Suno.ai, which has a waitlist for its foundation models for generative audio AI, has examples produced using Bark text-to-speech. And they are very impressive – so much so that this writer will be using Monster API to put the algorithm to the test for themselves.

How to use Monster API

Signing up to Monster API gets 2500 free credits, and TechHQ used the distributed computing service to generate the images seen in this article (pictures created in landscape mode used 7 credits, based on our research). To access the services you need first to generate an API key and bearer token and then cut and paste the code examples for the model that you want to use. In our case, we fired up Stable Diffusion’s text-to-image generation.

Send request: using Google Colab notebook to run Python snippet passing model parameters to text-to-image generative AI over Monster API.

Google’s colab notebooks are a convenient way to run Python in a browser. And pasting the API key and bearer token details into the code, together with the text-to-image prompt and other model parameters, successfully produced a POST request sent to the distributed cloud compute with a process id.

Collecting the image results: once you have the process id, you can issue a fetch request and download the generative AI model output.

The next step, to retrieve the generative AI image, is to issue a fetch request – again using a snippet of Python code – including the process id, as well as API key and bearer token details. Running the colab notebook cell produces a message saying that the fetch request has been processed successfully, and the status is changed to completed. The output includes a weblink that allows the user to access and download the text-to-image generative ai model result.

So, before considering a mainstream cloud computing provider, you might – if the idea of Airbnb for GPUs appeals – consider a distributed, crowdsourced alternative. And the payback could be attractive if the business model works as promised.

The post Airbnb for GPUs – decentralized IaaS for generative AI appeared first on TechHQ.

If you had to pick a list of facilities management topics regularly discussed by staff, office temperature would rank high – possibly in the top spot. And one way of settling any arguments is to attach a thermometer to the wall. But thanks to industrial IoT sensors available as a smart building kit in a box, you don’t have to stop there.

Today, building operators – or anyone wanting to improve the management of their surroundings – can install a variety of industrial IoT sensors to detect not just temperature, but also humidity, signs of water, the proximity of objects or room occupants, and gather much more data besides. And don’t think that you have to arrange for complex cabling.

Smart building kit

Industrial IoT developer – Disruptive Technologies, headquartered in Lysaker, Norway – has shown that sensors can be the size of a small postage stamp, connected wirelessly, and simply stuck to the wall using a peel-off sticker on the back. And if this writer sounds confident of those facts, it’s because TechHQ has been putting the technology through its paces for the past six months as part of a long-term review of the firm’s ‘smart building kit in a box’ sensor starter pack.

Steaming hot: there are many applications for water monitoring IoT sensors. Image credit: JT, TechHQ.

In our unboxing article – which looked at the different types of industrial IoT sensors that were supplied in the pack and what was required to get the smart building kit up and running – we were impressed with how quickly users can begin logging data. All it takes to configure the setup, once the wireless hub has been plugged into a regular mains socket and powered up, is a few QR code scans to assign the sensors to a user account accessed over the internet through a web browser.

And the company-supplied kit has been happily gathering smart building data ever since. The number of possible applications is, in some respects, only limited by a user’s imagination. Even with just the five sensors supplied in the starter pack there are still a wide variety of applications that can be served. And you can get creative – for example, by using the water detector to sense when fresh coffee (based on steam coming out of the drinks machine) has been brewed.

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As mentioned in our interview with Øystein Moldsvor, Co-founder and VP of Engineering at Disruptive Technologies, the moisture-sensing capabilities of the firm’s industrial IoT sensors are being used to help protect historic buildings (such as the Royal Opera House in London, UK) by providing early leak detection.

The data dashboard, which users can log into via the web, allows operators to monitor the various information feeds remotely. And alerts can be configured to send maintenance personnel email notifications based on sensor triggers. The starter kit includes a touch sensor, which we programmed through the DT Studio online app – as part of our testing – to send an email when someone pushed it. And the system worked first time.

TechHQ test results: email alerts can be configured based on smart IoT sensor triggers.

Other users have been impressed too. Norwegian software developer Datec has partnered with Disruptive Technologies to integrate the industrial IoT sensors into a facilities application dubbed CleanPilot. “We use the sensors to automatically adjust the active cleaning schedule – presented to the cleaner through the iOS CleanPilot GO app – by adding, postponing, or cancelling cleaning tasks according to the actual use of the premises,” Geir Arne Hansen, Chief Administrative Officer at Datec, told TechHQ.

Technical partnership

Datec believes that need-based cleaning – enabled through easy-to-install smart building kit and companion planning, operation, and optimization software – can increase cleaning efficiency by 30%. Operatives can view the paperless system using iPads. And a web portal gives managers real-time data on cleaning activities throughout the facility.

In the Datec solution, wireless proximity sensors provided by Disruptive Technologies are used to measure the number of people passing through different parts of the building. And this information is used to generate cleaning plans based on actual needs. For example, teams can focus on areas that have been used that day rather than working to a sub-optimal strategy of spreading cleaning time equally across the whole facility.

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Customers include hospitals and government agencies, and it’s another example of how industrial IoT sensors are helping to streamline facilities management and buildings operations. As well as the motion sensors featured in the Datec CleanPilot integration, other available wirelessly connected peripherals include desk occupancy sensors.

These unobtrusive electronic devices make it easy to understand office utilization in real-time – for example, to help with space planning for hybrid working patterns. And, like all of the sensors, these small-sized units have an incredibly long battery life (8 years in the case of the desk occupancy design) thanks to clever, energy-efficient wireless transmission protocols.

Also part of the product line-up are wireless carbon dioxide (CO2) sensors – with a battery life of 10 years – that can, together with temperature, humidity, and other measurement feeds, keep track of environmental factors that impact workers’ health and productivity.

Having a plug-and-play eco-system that lets anyone – not just developers – explore the benefits of smart buildings is a game-changer. And as more users share their IoT sensing stories, others will undoubtedly be tempted to give the technology a go. We’ve had great fun putting the sensor starter kit to the test, and it’s an ideal launch pad for smart building ideas.

The post Industrial IoT sensors: smart building kit in a box appeared first on TechHQ.

Robots have long made headlines, first in science fiction and then as science fact, as movable electronic devices have grown in sophistication and been put to work in the physical world. And one of the strongest robotics trends in 2023 is the rise of collaborative robots – a class of automated machines dubbed cobots, which are designed to work alongside humans and can accurately perform repetitive tasks.

What are cobots used for?

Today, cobots can be found in numerous industrial settings. They are well-suited to carry out common actions in manufacturing, device assembly, food production and processing, goods packing, and various other commercial scenarios involving repetitive manual work.

Well-known manufacturers of cobots include –

• Kuka
• Universal Robots
• AUBO
• Fanuc
• Franka
• ABB
• Techman Robot
• Hans Robot
• Kassow Robots

Reporting on the collaborative robotics sector, market analyst IDTechEx has identified at least 30 different cobot developers. Firms offer products ranging from highly integrated high-cost cobots, retailing for more than US $35,000, through to more affordable lower-cost robotics priced at below US $25,000.

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And customers can easily try before they buy by hiring cobots – for as little as £65 (approx. US $80) per day, in some cases – based on figures advertised on the web and depending on the device.

Looking beyond what cobots are used for, it’s important to consider the features that have made these machines so successful. Industrial robots have been around for decades, but – for safety – they operate separately from humans and halt for employees. The productivity gain enabled by cobots is their ability to share spaces with staff and collaborate with workers on tasks.

In addition, and emphasizing why cobots are a strong robotics trend in 2023, is how straightforward machines are to program. And once clients understand how to program a cobot they will almost certainly come up with many other ideas on how machines can further automate their operations.

How to program a cobot

Cobots can be fitted with a wide range of peripherals from arc welders to grippers. And units can also be kitted out with additional sensing elements such as machine vision hardware so that cobots can make intelligent decisions based on what’s in front of them.

Customers are becoming increasingly creative in how cobots can be used – for example, San Francisco, US, based Café X uses a US $25,000, 6-axis robotic arm to prepare and serve coffee to customers. And when the unit isn’t busy, it’ll entertain passers-by with a pre-programmed dance.

Enjoy a unique coffee experience with a robotic twist! Cafe X is a robotic coffee bar that offers a variety of drinks for you 24/7.

T3 by F4
T1 by B20 #SFODining #Coffee #Robots @getcafex pic.twitter.com/00KxDbA8eW

— San Francisco International Airport (SFO) (@flySFO) June 6, 2023

Another firm exploring the commercial potential of coffee and robots is Chinese firm Shanghai Hi-Dolphin Robot Technology. The developer offers a cobot-staffed drinks stand dubbed COFE+ that can make five cups of coffee in parallel and, according to the firm, units have been exported to the US, Germany, Australia, and Southeast Asia.

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Robot baristas highlight cobots versatility. Machines are compact – incredibly so, if you compare cobots to early industrial robots – and capable of carrying out repeatable and precise movements. Cobots are also relatively mobile and can be used to perform different tasks at different locations on the production floor.

Sticking with the food and beverage industry, another trend is the growth in so-called high mix low volume (HMLV) production. Food and drinks producers are exploring a greater variety of products, manufactured at lower volumes, appealing to consumers’ taste for a wider range of goods, but on a similar theme. And this requires a higher level of packaging automation to manage the increased product variety.

“As packaging requires robots with a low payload and high flexibility, it can be an ideal use case for cobots,” write IDTechEx analysts Yulin Wang and James Edmondson in their report, Collaborative Robots (Cobots) 2023-2043: Technologies, Players & Markets.

The future of cobots

Hospitality and healthcare are emerging opportunities for cobots beyond more familiar industrial use cases. And the rise of artificial intelligence (AI) – particularly given the success of advanced chatbots, such as OpenAI’s ChatGPT, powered by large language models (LLMs) – adds another dimension to the future of cobots.

LLMs are already automating knowledge work and it’s interesting to consider the impact those digital tools could have in the physical domain when those smarts are integrated into cobots. Giving robots the capacity to learn tasks for themselves, rather than having to be explicitly programmed, could radically reshape the kinds of jobs that humans will be performing in the future.

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For example, in construction remote-controlled equipment already allows just a few workers to assemble large building frameworks. And it’s reasonable to imagine greater use of collaborative robots to extend opportunities for automation, to lower cost, improve safety, and deliver other benefits in construction and elsewhere.

“Faster reaction time, more exact movement patterns, orientation capabilities, capabilities in imitating humans – all these aspects contribute to advancements in cobot development,” comments Elena Fersman – a computer scientist and AI expert based at Ericsson’s Silicon Valley site in the US, and a fellow of the Royal Academy of Engineering Sciences in Sweden. “When brain signals can be read with high precision and transferred to the robots, we will be able to collaborate with them in a completely new way.”

Over the years, AI has been regularly hyped as a game-changer for industry. But predicting when those successes are going to happen has never been easy. However, when AI does take off, it’s hard to ignore – as the current boom in digital automation shows.

In this case, the uptick in productivity is enabled by large language models trained on vast amounts of information scraped from the web, as well as digital repositories of books and academic papers, and with the possibility to be further fine-tuned using business data. And when those learnings cross over into the physical realm, possibly with advanced cobots as a first step, we will witness another huge leap in automation.

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Crop harvesting robots dream of soft fruits

Today, in the digital world, generative AI – which includes not just advanced chatbots, but also image creation applications – is impacting multiple professional fields. And the combination of AI and robots will have widespread ramifications for companies and their staff. However, there are greater constraints in delivering physical products compared with software-based solutions.

Returning to the example of cobot-run coffee stalls, Café X CEO and founder Henry Hu reminds the media that it’d take his firm more than 20 years (building 1000 automated kiosks per year) to compete with the number of locations that Starbucks has worldwide today. Even if you have robots building robots there are still time and materials constraints that don’t apply to digital products. And AI cobots, although likely to be highly impactful, won’t be able to proliferate at the same rate as LLM services are currently.

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Artificial intelligence (AI) has leapt into the limelight thanks to the rise of generative AI and large language models (LLMs) powering text-based image creation and advanced chatbots such as ChatGPT. But that’s by no means the last of the statistical wizardry enabled through deep learning and other machine learning methods. Digital AI skills will impact our physical world as those automation lessons inspire developers to build better robots.

AI is providing vast automation opportunities in the digital domain and is busy reshaping the labor market. “AI has cut positions, broken the bottleneck of human efficiency, reduced standardized and repetitive work, changed the nature of work and enhanced work efficiency,” wrote David He and Venessa Guo of the Boston Consulting Group in a report commissioned by the World Economic Forum. And those conclusions were drawn in 2018, four years before OpenAI had released ChatGPT and teamed up with Microsoft.

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Despite all of the analysis, the impact on jobs brought about by digital AI skills can still be hard to foresee. Today, few people would be surprised to learn that automation has radically changed the nature of mechanical work. But to discover that makeup artists and law professionals – to give just a couple of examples – could find themselves being displaced by generative AI tools is certainly more of a shock.

We are just beginning to experience the impact of AI on knowledge work as digital skills are becoming adopted more widely to automate all kinds of tasks, such as content generation for websites and article translation. And someone that understands the positive impact of AI on businesses is Jonas Angleflod, CEO of Stockholm-based Theories Group, a scale-up accelerator for internet ventures.

Crossover algorithms and how to build better robots

What makes Angleflod interesting too, is that he is a self-confessed robot nerd, and he points to a number of ways in which digital AI skills provide lessons on how to build better robots. Theories Group helps online firms to monetize content in different ways – for example, by developing comparison websites, identifying topics with strong user intent, syndicating content, and translating it for local markets.

“We use a lot of AI tools ourselves,” he told TechHQ. “There’s a wheel, a process flow that’s happening.” Today, investing in automation in the digital domain means investing in AI – for example, to build tools that can spin out content in different versions. But the human creative spark remains. It’s just augmented and data-driven so that websites can run with less manual intervention.

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Witnessing the impact of AI on digital tasks has got Angleflod thinking about how algorithms will cross over and augment our lives in the physical world in sectors such as healthcare, schooling, and emergency services. As knowledge of how to build better robots grows, AI could enter new verticals and help workers by performing dangerous tasks and eliminating tedious or repetitive work.

It’s an interesting thought exercise to consider what would happen when AI takes a physical form. Early success stories are relatively simple devices such as robot vacuum cleaners and autonomous lawnmowers, which – at scale – free up a large amount of time for humans to pursue more interesting endeavors. In a hospital setting, cleaning robots could perform sanitation tasks and help to prepare rooms for patients.

Fine-tuned biological machines

Building robots to carry out more complex tasks soon gets much more difficult and expensive. “Fifteen years ago, the problem was batteries,” said Angleflod. “But now it’s shifted towards energy efficiency.” Computers remain power-hungry in comparison to the human brain, which – although it contains some of the hungriest cells in the human body – still gets by with the energy input of a dim lightbulb.

“Nature has fine-tuned what a biological machine needs, but we’re still using motors and actuators that are not energy efficient and sensor efficient,” he points out. It’s still early days in finding ways to mimic skin and muscles and understanding how to build better robots that can navigate new surroundings and not bump into things – for human safety, as people are likely to be more fragile than future machines.

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“Small robot augmentation is a sensible first step,” said Angleflod. “We think that cobots are the first generation.” Collaborative robots (cobots) designed to work in spaces shared with humans have become an automation success story in manufacturing. They can be programmed to perform assembly, dispensing, finishing, quality inspection, and a whole host of other production tasks.

Just considering palletization (the process of stacking, loading, and securing high quantities of goods onto a pallet for storage and transportation) highlights how big an impact AI could have in the physical world. There are estimated to be two billion pallets in service worldwide, and loading and unloading them is a pain.

“Traditionally, palletization has been a manual operation – and in many cases it still is – we estimate that 250,000 people are employed in this type of work worldwide,” explains Irving Paz Chagoya of Universal Robots. “Manual palletizing requires these workers to perform the same strenuous task over and over again. Workers bend, lift and twist for hours on end, which can cause long term musculoskeletal damage.”

Digital AI skills and supporting new careers

Leveraging the power of AI and robotics will eliminate jobs, but if workers have the opportunity to upskill and build new careers then it doesn’t have to lead to unemployment. However, it could take time for things to stabilize based on how quickly ChatGPT – which was only released in November 2022 – has caused knowledge work to wobble as enterprises race to integrate LLMs into their operations.

Automating the physical world at a similar pace would have dramatic consequences, but – for now – there are too many hurdles still to overcome. And it’s possible that some lessons would have been learned from the application of AI skills in the digital domain, but relying on humans having learned lessons carries its own risk.

A future rich in cobots could be good news for device makers such as Apple and other computing firms who are profiting from digital applications of AI that make current products more attractive to consumers. But to succeed, incumbents would need to manage to transition their digital AI skills into the physical domain.

It’s possible that the added complexity of building robots could see new tech giants arise. Today’s robotics companies are often highly specialized and have been built up through large investments. And creating a general AI robot could be a task that’s beyond any individual firm, providing opportunities for multiple contributors and setting the scene for an industry that could look more like the automotive sector with OEMs and tiers of suppliers.

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• Automated webcams improve remote work meeting experience
• Optical sensors can highlight whiteboard content
• Linked cameras provide seamless coverage in large boardrooms

Web-based tools have made it easy to share information and collaborate on documents, spreadsheets, and other business applications. But there are still scenarios where the remote work audience can miss out on the in-person experience. And one of the biggest challenges for those dialing in is trying to keep track of what’s happening in busy conference room meetings.

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Video conferencing tools have evolved rapidly and are capable solutions for one-on-one interactions thanks to screen sharing and headsets that clarify the conversation for participants. But those setups fail miserably in a boardroom setting. Placing a conventional webcam or a laptop at the top of the table makes for mixed results and puts remote workers at a major disadvantage compared with those in the room.

Hybrid and remote working allows companies to recruit talent from anywhere, but if firms can’t make the most of those skills and ideas, then the rewards will fall short of expectations. Companies miss out on valuable insights and comments from participants joining online when conversations in the physical conference room are hard to hear and if in-person interactions are off-camera or poorly framed. But Owl Labs – headquartered in Boston, US – is on a mission to change that.

Competing on the best experience

Co-founders Mark Schnittman and Max Makeev had a eureka moment when seeing how meeting dynamics improved when a laptop was put on a swivel stool. And both knew that automation and software could make the experience even more interactive – fusing smart algorithms with readily available cameras and sensors developed for smartphones and other consumer devices.

By 2016 the duo, who had met as employees at iRobot, had their first prototype 360-degree camera, mic, and speaker device dubbed the Meeting Owl. And today, more than 150,000 organizations use Owl Labs technology to maximize the opportunities of remote work and make the goal of being able to work from anywhere a reality.

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Placed on the conference table at the in-person venue, the Meeting Owl gives remote workers a 360-degree banner display of everyone in the room. And to make interactions face-to-face, separate camera views focus on active speakers based on algorithms that respond to motion, vision, and voice signals.

Cleverly, two Meeting Owls can be paired, turning larger, harder-to-capture business discussions into a well-produced TV show experience for remote workers joining online. “You go from watching a bad movie to something that’s more engaging,” Max Makeev – Owl Labs Chief Development Officer – told TechHQ.

Edtech opportunity

Makeev and his colleagues have worked hard to create the best experience for people inside and outside of the room. And the technology isn’t just for business meetings, it’s proven to be a valuable edtech tool as well. Education providers have been quick to realize how useful the meeting technology could be to improve the remote study experience for students.

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“The Meeting Owl follows the teacher to the whiteboard, enabling hybrid learning,” Makeev comments. “It gives that feeling of being connected.” One of Makeev’s proudest moments was seeing how the technology improved the study experience for an immune-compromised student who had to isolate from their classmates. Thanks to the setup placed in the classroom, they were still able to see and hear the teacher and other students clearly and participate remotely.

A new addition to the remote work tech line-up is a whiteboard camera that integrates with the meeting technology to make the writing and drawings pop for online participants. Makeev demoed the kit, which is activated by rotating a QR code – one of two, placed on a regular whiteboard to mark out the physical area for the optical sensor.

Best view for everyone

With a regular webcam, whiteboard interactions – that may not be in the shot or in focus – are often lost to remote workers. Instead, staff joining online will likely have to rely on an in-person colleague sending a photo of the final visuals when it may be too late for their ideas to affect the outcome. But having a dedicated whiteboard camera changes that and makes annotations crystal clear, rendering pen strokes and coloring digitally on-screen.

Remote work isn’t disappearing. In fact, the Owl Labs annual ‘State of Remote Work Report’ – a collaboration with Global Workplace Analytics – found that the number of workers choosing to work remotely in 2022 was up 24% on the previous year. And staff with hybrid work schedules had risen by 16%. The trend is clear, and with two-thirds of employees questioned saying that they would immediately start looking for a job with more flexibility if the ability to work from home was taken away, remote work isn’t about to disappear.

Companies that will benefit the most will be those that have meeting technology that delivers the best experience to participants regardless of where they may be. And technology from Owl Labs and other providers is shrinking the gap between being in the office and working remotely.

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Since its debut in November 2022, ChatGPT, a large language model (LLM) powered by OpenAI’s GPT algorithm, has garnered significant interest. In its latest commercially-available form, GPT-4 builds on the foundation of its predecessor, GPT-3, having utilized larger datasets and increased computational power during its training cycles. Models were fed vast amounts of data from various sources, including scientific archives, books, reports, forums, and news coverage. That’s enabled OpenAI to develop increasingly accurate statistical models of language and knowledge.

The GPT series of LLMs are highly effective due to using attention mechanisms to predict the next word in a sequence. GPT-4 has a context length of 32,768 tokens (equivalent to over 50 pages of text), enabling it to summarise lengthy documents and engage in thought-provoking discussions.

API access to these advanced language services now has the potential to transform enterprise software, including – the subject of our focus today – RFP automation tools. That’s transformative because AI already offers the ability to address complex RFP documents and helping guide companies responding via RFP to tenders.

The extension of existing capabilities with LLMs like ChatGPT4 is likely to disrupt how companies respond to all types of tender documents. That will happen not just through the emergence of more powerful models but in how more extensive data is collated locally, and the complete dataset is quizzed using natural language.

Source: Avnio

Knowledge Library

Pre-trained models can be fine-tuned with corporate data – keeping the information sandboxed for security – allowing LLMs to respond to not just general questions but highly detailed business queries that pertain to a single company. Even answers to questions not experienced before can be created by AI routines. This capability, which is still uncommon, already delivers huge productivity gains. For example, Avnio – a provider of RFP automation software – finds that using AI to generate executive summaries, cover letters, and autocompleted business details can reduce the time its clients spend on RFP, security questionnaires, and other company requests. The majority (~90%) of questions in tender documents can be answered automatically using a combination of AI algorithms that mine pre-taught models combined with data local to the responding company.

Its users can take an RFP, including attachments in any file format, and drop the electronic documents into a Salesforce-native workflow. The application uses language recognition to understand digitized paperwork and structure the various information fields into categories. The tool can identify whether entries relate to security or compliance topics, legal considerations, and queries on company structure, to give just a few examples.

Linking the incoming requests to an organization’s knowledge library allows the RFP automation platform to generate a first draft in seconds. A company’s library of RFP question responses will build rapidly, thanks to AI-powered contextual interpretation and response creation.

Users already have the option to edit and finesse RFP responses generated by AI right in Salesforce, plus they can raise queries through an assistant and make quick edits via Slack and Teams. Smart automation can only improve win rates as the AI platform in the back end improves over time.

The qualification rounds

Automation tools give bid writers more time to focus on the proposal’s key writing and editing stages and put their organizations ahead of the competition. The autocomplete function in the Avnio tool is more than just a time-saver. The answers provided by humans as responses to the RFP document are also analyzed by the Avnio platform to determine how accurately the answers match the questions on the RFP (or other input documents) – shining a light on the company’s propensity to win new business.

Wider data resources from Salesforce inform the RFP response process with information like specific consultants’ win rates and contract value, region, contract type, and competitor data: in fact, any way that data can be sliced and diced.

The system’s use of machine learning can help determine whether the tender is a good fit when the company’s capabilities and resources are considered. This is output as a ‘qualification score’ in a percentile, allowing Avnio users to yes-no RFPs much more quickly, and letting them concentrate on projects where win rates are predicted to be much higher.

For companies considering high numbers of RFPs and other information requests, this scoring step allows them to quickly prioritize which questionnaires to place at the top of their to-do lists. And by optimizing their RFP processes, users will be more likely to win more business.

Companies can update their Salesforce-based knowledge libraries with the information and insight gained from submitted proposals. And while firms will, naturally, be keen to win rather than lose bids – the feedback is useful regardless of the outcome.

Native to the Salesforce environment, Avnio’s platform, which is available on the AppExchange, can pull in information from Salesforce objects and use those databases to identify patterns and supply supplementary data that enriches responses to RFPs.

Source: Avnio

Wider organizational benefits and the Future of Automation

As GPT comes online, data analysis will become available via natural language interfaces. Queries around underlying or hidden patterns to RFP success rates could be couched in simple queries: “How is our record of success in the Sustainability section of RFPs in this sector we’ve submitted in the last three years?”

The power of GPT4 means that any company leveraging it in commercial contexts can draw from the wide learning corpus that has trained the model, including academic papers and statistical analysis. Therefore, as the technology comes online in the RFP space, queries could compare, for example, the company’s resources and RFP responses against those of competitors. Insights from the correlations of massive data sets will be just a natural language query away.

Present-day Next Steps

Bid managers, bid writers, tender management teams, and heads of deal structuring will all benefit from the focus on what’s succeeded in responses to date and, more to the point, why. Insight into where the company is missing out will improve win metrics rise, thanks to unearthed trends that the Avnio AI-powered RFP automation platform delivers.

Avnio and Salesforce recently discussed RFP automation in a live webinar with a key focus on the possibilities of GPT4 and the company’s roadmap toward its integration into RFP processes. You can catch it here.

The post How AI is transforming RFP responses (and the GPT future) appeared first on TechHQ.

• Quantum algorithms optimize container logistics at Port of Los Angeles, US
• Industrial applications of quantum computing more accessible to clients
• Customer list growing as case studies highlight advantages to firms

D-Wave is keen to stress that users don’t have to wait for future developments to benefit from quantum computing. And the North American firm, which has been commercializing devices for decades, has multiple examples of enterprise clients using quantum computing to optimize business operations. For example, PayPal has used systems to accelerate fraud detection. And there’s now a long list of global companies using quantum bits (qubits) to rapidly solve complex problems, emphasizing the many industrial applications of quantum computing.

To help commercial users understand just how effective quantum computing can be at addressing industrial applications, D-Wave works with customers through its professional services division. And the firm’s revenue is split between charges for those engagements and quantum computing as a service (QCaaS), which enables users to run their production applications on the latest hardware.

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Naturally, as more users get up to speed on the possibilities, D-Wave expects to see a larger share of its revenue come from QCaaS – possibly as high as 90% within five years. And the company’s quantum cloud service, dubbed Leap, is available in 39 countries, with users having run more than 50 million problems on its Advantage quantum annealing architecture since September 2020.

D-Wave’s Q1 2023 results show that revenue derived from commercial customers is up by 30% as firms explore industrial applications of quantum computing. And looking at the client list gives a snapshot of the types of sectors that are benefiting from the power of qubits to quickly determine the most favorable solution to industrially relevant problems by considering a huge number of permutations in parallel.

Industrial applications of quantum computing

Besides the already mentioned PayPal, high-profile quantum computing customers include Volkswagen, DENSO, Johnson and Johnson, GlaxoSmithKline, POLARIS^qb, NEC Corporation, Mastercard, Deloitte, and Save-On-Foods – to give just a few examples. And what’s attracting industrial clients is the capability of quantum computers to solve optimization problems.

“These are things like employee scheduling or autonomous vehicle routing for manufacturing plant for optimization or bin packing containers on ships or on freights to improve supply chain operations or peptide designed for use in new drugs,” explained Alan Baratz, CEO of D-Wave, in an analyst call.

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Quantum annealers, as Baratz – who joined D-Wave to run the company’s R&D organization and then, a few years later, took over as CEO – explains, can be thought of as optimization engines. And they are one way of putting physics to work to solve complex computing problems more efficiently. By finding the lowest point across a multidimensional landscape (formulated to capture the details of the industrial task, such as finding the shortest route between logistics hubs) annealers can rapidly point to the lowest energy (or optimum) solution.

Problems are formulated as either an Ising model or a QUBO model, which begs the question – what are Ising and QUBO models? The (very) short answer is that they represent ways of breaking down a real-world problem into something that D-Wave’s quantum computing architecture can solve. And compared with configuring quantum gates (another way of building algorithms out of qubits) they offer a relatively easy on-ramp for exploring the industrial applications of quantum computing

A closer look at the programming model

Taking a closer look at D-Wave’s programming model provides more detail on the setup process. The model features five key elements: qubits (which settle into one of two final end states), couplers (that enable qubits to influence each other), weights (used to influence the likelihood of the qubit’s final state), strengths (a modifier to influence the control that one qubit has on another), and the objective to be minimized by the system.

Other examples of real-world problems that could be optimized include satellite positioning so that there’s no conflict between orbits. Or stock selection to minimize the risk of holding too much of a particular equity. And setting the programming parameters appropriately will favor solutions that users desire (for example, orbits that keep satellites apart) and penalize scenarios to be avoided (such as collisions in outer space).

Annealers will sample from the range of outcomes to present the optimized or lowest energy solution. And D-Wave has a wide range of open-source resources that provide more information, including a problem-solving handbook. Plus, at Qubits 2023, partners shared a range of industrial applications of quantum computing to highlight how customers are using these tools practically.

A great example is how SavantX, a data analytics firm based in the US, has used quantum computing to dramatically improve logistics at the Port of Los Angeles – the busiest container port in North America. In its approach, the team leveraged the power of quantum annealing – expressing the problem of scheduling trucks to collect containers to maximize throughput and minimize the distance travelled by rubber tyred gantry (RTG) cranes, on a multidimensional landscape for optimization. And the results of the exercise are impressive. Thanks to the algorithm, operators were able to double throughput and almost half RTG movement, which is good news for both profits and maintenance.

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