Navigating the complex world of international trade can be an expensive endeavor for businesses of all sizes, thanks, in part, to the duty costs on imported goods. Depending on the products’ value, type, and origin, charges range from just a few dollars to a substantial portion of their price. As a result, organizations can end up spending a considerable amount of time analyzing the duty rates of their target markets and investigating potential trade agreements or preferential arrangements to optimize their supply chains just to remain competitive. Fluctuating trade policies and geopolitical developments add an extra layer of uncertainty, making it challenging to forecast import costs accurately.

These headaches can push companies towards third-party brokers that will handle the process for them in exchange for less than a hundred dollars per consignment. Brokers are experts paid to be aware of the ‘Special Procedures’ put in place by each country.

What are ‘Special Procedures’?

Special Procedures are duty regimes that allow you to store, temporarily use, process, or repair your goods and get either suspension, partial, or complete relief from import duty. They are designed to simplify trade movements across borders, working to promote international trade and foreign investment.

Types of Special Procedures include Inward and Outward Processing Relief (IP and OP), where import tax benefits are applied to goods that are temporarily imported to or exported from a country for processing or manufacturing purposes. There is also Customs Warehousing (CW), where goods can be stored in a customs warehouse without the need to pay customs duties or taxes until they are released for sale in the local market or re-exported.

Source: Shutterstock

By leveraging such procedures, a business can optimize cash flow, reduce upfront costs, and strengthen its position in international trade by strategically managing customs duty liabilities. However, doing so without the help of a customs broker is no mean feat.

The rules and regulations governing Special Procedures can be complex and vary from country to country, and it can be easy to slip into compliance traps which result in penalties, delays, and additional costs. Staying up to date with the latest procedures is also time-consuming, as they can evolve over time due to trade agreements, geopolitical developments, or policy changes. Indeed, to have Special Procedures authorized, a company needs to report its customs movements to ensure the traceability of the goods, which can be cumbersome without specialized tools.

Using a broker can save businesses time and effort as they will seek out cost-saving opportunities through Special Procedures on a client’s behalf, ensure proper compliance, and reduce the risk of errors. But the economic costs can make this option unfeasible in the long-term. Regardless, it’s a slow solution that doesn’t scale and keeps much valuable business data away from the declarant.

It’s also common that a broker will only be conversant with procedures in some of the countries where declarations have to be lodged. For the remainder, further brokers need to be sourced, leading to a complex web of contacts that’s difficult to manage.

However, recent technological developments in automation have created an alternative that may be preferable.

Automating customs management

Customs management platforms automate tasks such as data entry, classification, and document preparation, simplifying the customs declaration process. These solutions often provide real-time updates on trade regulations, ensuring businesses stay informed and up-to-date. Embracing such technology allows companies to tap into the benefits of Special Procedures without the need for outside expertise.

Source: Shutterstock

A leading cloud-based customs clearance platform is the Customs Accounting System, or CAS, from Customs4trade (C4T). CAS automatically standardizes and consolidates customs data from different countries with varying requirements in a centralized platform. This allows businesses to simultaneously file export declarations in the country of departure and create import declarations in the destination country using a single message.

With built-in compliance, CAS keeps legal content up-to-date, safeguarding customs management from legislative changes. CAS adheres to global standards set by the World Customs Organization (WCO) and the Universal Customs Code (UCC), ensuring widespread acceptance. Its powerful visibility and analytics tools grant users real-time insights into customs compliance data, facilitating informed decision-making.

The digital solution for Special Procedures

Gaining authorization for Special Procedures, and reaping the associated benefits, requires advanced administration capabilities. These include real-time records of stock balances and the ability to produce detailed reports for customs authorities.

CAS’ native-cloud solution has a dedicated Special Procedures module which monitors inventory and produces the reports and declarations required to demonstrate administrative and regulatory compliance. The stock records necessary for authorization are compiled by combining data from customs declarations with movements received directly from the company’s ERP or WMS source system. Equivalent information for processed products can also be tracked using data extracted from Bills of Materials or Production Orders.

The CAS Special Procedures module allows the user to benefit from automation. By automatically selecting stock records, suggesting the suitable customs procedure to fulfill the authorization, and initiating the required declaration, it streamlines the entire customs compliance process. Moreover, it optimizes operations by automating the selection of duty-advantaged free goods, or bonded goods, resulting in cost savings and increased profits.

Car manufacturer Honda is one of many recognizable brands that have adopted CAS and have been able to take advantage of cost-saving opportunities provided by Special Procedures. The UK’s exit from the EU resulted in increased duty costs which can be avoided with Special Procedures authorizations. Rather than taking on the huge administrative workload required to qualify for these benefits, Honda implemented IP and CW through CAS. This enabled it to calculate duties based on origin and final destination rather than paying full duty on materials sourced from other countries and processed in its plants for export.

With CAS’s automation, Honda streamlined customs declarations, ensuring real-time processing of car parts and maintaining maximum production efficiency. The Special Procedures module allows Honda to track customs stock movements, optimize duty costs, and stay compliant with reporting requirements.

To learn more about how your business can benefit from Customs Special Procedures, download the Insiders’ Guide from Customs4trade.

The post Something to declare? How ‘Special Procedures’ can save you money on import duties appeared first on TechHQ.

Operating systems have a huge bearing on our relationship with technology and appeal to personal preferences – for example, try getting Linux, Mac, and MS Windows users to swap machines! And one of the most significant operating systems in our daily lives is a platform type that many of us never consider – the terminal operating system, which is critical to transporting goods efficiently around the world.

Experience goes a long way when it comes to implementing a terminal operating system that’s going to achieve its full potential. And, as customers soon discover, one size doesn’t fit all. The selection process begins with the nature of the shipping terminal as break bulk – goods such as steel, lumber, and agricultural products, which are not shipped in containers – processes deviate from general cargo operations.

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David Trueman, MD of TBA Group, points out that container processing involves standard dimensions – so much so that operations can run efficiently with little knowledge of what’s inside. Container terminals also benefit from a standardized format of electronic data interchange (EDI) and suit optical character recognition – with agreement on the type and position of container numbers.

However, break bulk cargo comes in various shapes and sizes. Plus, it’s vital to know the nature of the goods to manage unloading, warehousing, and transport. And cargo identification markings are more varied, both in design and location.

“It’s really important to understand where the data sources are going to be,” Trueman responds, when asked about the single most important thing to consider in the design of a bulk handling terminal operating system. “Where are you going to get your real-time information? The location of weighbridges in the operational workflow is vital.”

What is a terminal operating system?

One way of picturing terminal operating systems is to think of them as an enterprise resource planning solution (ERP) for port operators. The systems are essential for optimizing labor allocation and equipment usage and managing the way that port areas are utilized. And Thetius, a maritime technology analyst firm, estimates that the terminal operating system market is currently worth over half a billion dollars.

Day 20 of the #30DayMapChallenge – My Favourite. I think it is the prettiest visualisation I have and indeed, will ever make!
Made with #Python using #matplotlib and #Numpy. #DataScience #DataVisualization #Map #shipping #Geography #dataviz #data #DataScientist pic.twitter.com/8iZiCfib62

— Python Maps (@PythonMaps) November 20, 2022

Features offered by vendors include fleet management, autogate systems, and video analytics. Terminal operating systems can build off industrial IoT frameworks to gather even more data on real-time operations – which expands the possibilities for machine learning and AI. And modules can service billing and other related activities to streamline business operations.

Also, given that vessel plans involve multiple parties, including the next port of call, collaboration is key. And terminal operating systems can help to manage that complex process, carry out better planning, and compile all of the necessary information into the right format, noting EDI requirements.

List of TOS vendors

• Contpark
• CyberLogitec (OPUS Terminal)
• DP World (CARGOES)
• Envision
• GullsEye
• Hogia
• Inform
• iPortman
• IT Partner
• Kaleris (Navis)
• MarineBerth
• Orbita TMEIC
• TBA Group
• TGI
• Tideworks (Mainsail)

Clearly, the world is becoming more automated. And port terminals are no exception from discharging and loading machinery handling vessels at the berth area to yard operations and gate management.

It’s commonplace – for example, in giant terminals such as the Port of Long Beach in the US (the country’s first fully automated port) or the Port of Rotterdam (Europe’s largest seaport) – to see self-driving container trucks (terminal tractors) shuttling back and forth. And reports suggest that smart ports brimming with IoT sensors could accommodate autonomous ships by 2030.

China too has been busy automating its port facilities, including Qingdao – a major seaport in the east of the country and one of the top 10 in the world based on traffic. Qingdao harbor has four zones, which handle cargo and container goods, including oil and petrol tankers, as well as vessels carrying iron ore.

Logical upgrade to supply chain planning

The scale of traffic, diversity of goods, and multiple modes of transport, including road and rail freight, highlight the demands that terminal operating systems have to meet. And getting to grips with this complexity helps to explain why ports are becoming a magnet for the latest technology.

On TechHQ we’ve written about how quantum computers are being used to plan the loading of trucks to reduce the distance traveled by RTG cranes and dramatically reduce maintenance and operating costs.

Private 5G networks are also helping to boost the efficiency of shipping terminals where mobile coverage may otherwise be patchy and feature dead spots. And there are gains beyond connectivity, as operators benefit from being fully in control of communications.

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Having a terminal operating system to measure and record port activity gives management a dashboard view on whether operations are achieving their key performance indicators (KPIs). And, particularly if KPIs are not being met, analysts can dive in – aided by data insights – and identify where the bottlenecks are.

Systems also provide a suite of reporting tools – for example, showing terminal inventory, gate movements, vessel movements, crane productivity, truck turnaround time, and much more.

The scale of modern freight shipping is mind-blowing. If you put all of the containers from a large category vessel onto a freight train – that freight train would be over 70 miles long.

And, typically, all of that cargo will be unloaded and replaced with waiting goods in less than 48 hours, which is a tribute to numerous advances, including developments in terminal operating systems.

The post Supply chain planning – the importance of terminal operating systems appeared first on TechHQ.

• Dining in at fast food restaurants is becoming a thing of the past.
• Chains are removing seating to optimize for takeaway pick-up and delivery.
• The future consists of robot chefs, drone delivery, and anti-sog packaging.

When you imagine a fast food restaurant, what do you see? A sparkling, plastic-and-tile establishment echoing with the squeals of children’s birthday parties and sniggering teenagers? Or queues of silent adults standing behind freestanding touchscreens or an unattended kiosk?

The answer probably differs depending on the last time you set foot in a McDonald’s as, over the past few years, the latter has become a more accurate representation. Technology is turning the sound down, as customers order over the phone or through a touchscreen, stand and wait to pick up their order before grabbing their food and racing outside – all without saying a word.

There has been a notable change in the number of visitors eating at fast food chains, which was accelerated by COVID-19. According to data from the NPD Group,  just 14 percent of US quick-service restaurant traffic is now dine-in – just half of what it was pre-pandemic. The following year, 85 percent of all fast food orders were taken to go.

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This is working towards a change in fast food restaurant culture. While the so-called ‘Golden Arches’ was once the destination, it is now just a quick stop along a pre-existing journey, if that. Many who want to enjoy some salt, fat, acid, and heat need only open their phones to have it delivered to their door within minutes.

As a result, chains are reducing the number of tables available for their customers, optimizing the space for on-premises orders, takeaways, and drive-thrus instead. This includes more drive-thru lanes and windows specifically for third-party delivery pickup.

Chipotle already offers designated drive-thru lanes for mobile-order pickups only, and other establishments, like McDonald’s, Burger King, Taco Bell, and KFC, are eager to roll them out too. Source: Chipotle

Fast food – standing room only

TGI Fridays introduced ‘Fridays on the Fly,’ a 2,500-square-foot store format that focuses on delivery and takeaway orders, early last year. Chipotle already offers designated drive-thru lanes for mobile-order pickups only, and other establishments, like McDonald’s, Burger King, Taco Bell, and KFC, are eager to roll them out too.

In fact, McDonald’s has already set up an ‘Order Ahead Lane’ at a branch in Fort Worth, Texas, that is nearly 100 percent automated. The restaurant was opened in December last year and has no indoor seating at all, instead containing special kiosks and digital screens where customers can place their orders to-go.

It also has a special pick-up shelf and a room dedicated to serving delivery drivers. Finally, it has parking spaces available for curbside pick-up, meaning anyone can have a warm meal within minutes of arrival.

McDonald’s in Fort Worth, Texas has a special pick-up shelf for mobile orders and a room dedicated to serving delivery drivers. Source: McDonald’s

The restaurant was opened in December last year and has no indoor seating at all. Source: McDonald’s

Four months after the Fort Worth branch was opened, it was reported by the Wall Street Journal that McDonald’s would be laying off hundreds of employees as part of a company-wide restructure. A memo sent to staff said that the layoffs were intended to make McDonald’s more efficient.

While most of those impacted worked at the corporate offices rather than in branches, the restructuring was said, at least in part, to “accelerate the pace of… restaurant openings” and “modernize ways of working.” Who knows what changes will come next to help achieve these goals?

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It is clear that most fast food chains are making efficiency improvements a top priority. Wendy’s is piloting “Wendy’s FreshAI” to take orders at drive-thrus and an “underground autonomous robot system” which will see bots delivering orders from kitchens to parking spots. Starbucks plans to open 400 new takeaway or delivery-only branches in the next three years, according to the Wall Street Journal, after removing all of the seating in select cafes.

McDonald’s is also the latest restaurant to utilize ‘geofencing’ – where back-of-house staff are alerted when a customer is approaching the restaurant to pick up their order with their location data, ensuring the food can be ready and warm upon their arrival.

If this trend of shifting towards delivery service is anything to go by, it seems that fast food fans are willing to accept the 30 percent price hike on food ordered through a third-party app, like Deliveroo or Uber Eats, for the comfort and convenience of having a meal in on the sofa.

But if you take into account travel costs, dine-in taxes, and the temptation to go and spend more money at other establishments after eating, does it really end up the more expensive option? Besides, your ludicrous energy bill needs to be paid regardless, so you may as well feel the benefits of it by staying in.

No pickles, no people

The battle for automation rages on, and there are countless technologies that are just waiting to be rolled out more widely. A robot chef called Flippy, from Miso Robotics, can reportedly flip burgers faster than a human while maintaining consistent quality. The bot is being used by White Castle, CaliBurger, and Inspire Brands, the parent company of Buffalo Wild Wings, Arby’s, and Sonic.

Starbucks has already spent millions on AI-powered espresso makers, which can mix brews more quickly than a human barista, and plans to invest even more in the area. The Blendid autonomous smoothie kiosk, which allows customers to order custom fresh drinks via an app before a robot arm gets to work with fruits and vegetables, provides a glimpse into the future of food stalls.

Short, Tall, Grande, Venti… and Robot?

Special packaging is being developed that prevents food from getting soggy over longer periods of time, allowing delivery drivers to take on more orders during their routes.

But those doing the delivery may not be human either. Starship Technologies’ fleet of autonomous ground vehicles currently deliver groceries in cities in the UK and US. They each have ten cameras, GPS, and inertial measurement units, as well as microphones and speakers to interact with customers. Their LIDAR systems provide a 360-degree view of their surroundings, enabling them to navigate pavements and objects to reach their destination.

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In Shenzhen and Shanghai, China, food delivery giant Meituan has been using drones to fly meals between skyscrapers for over a year.

Autonomous food delivery robot from Starship Technologies. Source: Starship Technologies

A food-delivery drone from Meituan. Source: Meituan

Where to now?

The poignant atmospheric difference between today’s fast food joints and those of, say, 2002 could have more consequences than just a slightly less happy Happy Meal experience. Many of these branches, McDonald’s particularly, tend to be places favored by vulnerable people.

They are often open all night, are well-lit, have sockets to charge your phone and free Wi-Fi, are cleaned regularly, and serve affordable food. Some have been designated ‘Safe Spaces’ in UK cities, where help is offered to those that need it.

It is unlikely that the ‘virtual restaurants’ of the future – McDonald’s has filed a patent for joints accessible only via a virtual reality headset – will provide similar sanctuary.

The post D’you want humans with that? How the fast food industry is turning to AI appeared first on TechHQ.

Twelve months ago, Frost & Sullivan made the case for why private 5G networks will be game-changing for some companies. And Troy Morley, an Industry Principal at the business consulting firm, believes that over the next decade private 5G networks will evolve to support the needs of smaller businesses in almost all industries.

What makes a private 5G network the right choice for firms?

Installing a private 5G network addresses a series of pain points that businesses may face. The first is network coverage. Cell towers in public mobile networks are primarily located based on demand – for example, across urban sites with large populations and alongside transport routes.

And this arrangement is fine for companies based in metropolitan areas or near the highway. But what if firms have business interests in remote locations that need to be connected locally and to headquarters? Extreme examples are underground operations such as mining and activities out at sea.

Telecoms trend: 5G antennas being installed on a building in South Korea. Image credit: Ericsson.

It may also be the case that firms near a public mast have great coverage outside, but experience dead spots when trying to use the cell network inside. Morley notes that factories and warehouses can have problems in this area, with buildings and their contents acting as potential sources of signal interference.

Given these network coverage concerns, it’s easy to understand why early adopters of private 5G networks have been operators in mining, energy, and manufacturing industries. There’s also data security to consider, which is another reason for firms to opt for a private setup rather than build solutions using public mobile network infrastructure.

5G performance gains

5G brings faster download speeds and low-latency performance to devices. And high-definition video and mobile gaming are well-advertised as reasons for mobile customers to ditch their old smartphones and buy new 5G handsets. But this is just scratching the surface.

“While most consumers think that 5G is all about them, the truth is 5G is ideal for addressing the networking needs of business and enterprise,” writes Morley.

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For example, 5G brings significant edge capabilities. Private 5G networks don’t just connect staff, they enable industrial IoT communications too. And wireless networks provide flexibility to make businesses more agile and composable – in other words, tools and teams are easier to reconfigure for different projects.

Cellnex’s Catherine Gull lists automation, worker safety, and situational awareness as the top three benefits that private networks can bring to operations. Enterprises can use 5G systems to automate indoors and outdoors, from self-driving vehicles to factory robots.

“They do it to increase safety, and they do it to increase reliability,” she told UPTIME attendees in June 2023. “The more of these robots that you put in one single space, the more other mechanisms of connectivity fall down and become unreliable or unsafe.”

Gull makes a strong case that systems such as private 5G networks give users reliable bandwidth and, for firms, can be ‘where they want them, when they want them, and how they want them’. And companies are no longer held back by the downsides of basing their operations on a public mobile network.

Laying the tracks to 5G-powered industries! Hungary's EWG Intermodal Terminal partnered with #Huawei to deploy a powerful private #5G network to manage freight, automated cranes, and staff management, completely changing the game in rail industry safety and efficiency.… pic.twitter.com/k0rh0JGm1b

— Huawei (@Huawei) July 26, 2023

Adding to the appeal of being in full control, firms may find that they are able to stack multiple use cases on private 5G networks. Beyond automation, systems can also enable asset tracking, help with training, streamline maintenance, and provide ERP integration – to give just a few additional applications.

Once businesses have the fat bandwidth that 5G offers, there’s a lot that they can do. And low latency (plus video over wireless) opens the door to accurate remote control, which has broad appeal across a wide range of industries – from logistics to healthcare.

“Most enterprises start with something that is really key to them and that’s oftentimes connectivity availability,” Gull points out. “And once that’s resolved, you can build on that.” Cellnex, headquartered in Spain, has more than 138,000 sites on which mobile network operators (MNOs) put their infrastructure. And it has cell towers located in 12 countries.

What private 5G network architecture do you need?

The multiple antennas associated with 5G infrastructure enable powerful beamforming capabilities. Signals from multiple 5G radios can be purposefully overlapped and grouped together. And regions of constructive interference in the emissions can even be steered toward devices by adjusting the phase of each of those broadcasts.

As the name suggests, mobile networks are ideal for maintaining connections on the move, and beamforming adds further precision to the technology. Using beamforming methods, signals can be tuned to follow devices. Buildings can be utilized too, as reflective surfaces to bounce mobile signals to recipients.

One of the trade-offs of using much higher frequencies, which offer more bandwidth, is that these shorter wavelength mobile signals don’t travel as far. But beamforming has been shown to compensate for this, putting suitably configured 5G systems on par with longer wavelength 4G networks, in terms of coverage – at least at the lower end of the 5G spectrum.

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Technically, to use 3GPP (the standards group for mobile broadband) terminology, a private 5G network is dubbed a ‘5G non-public network’, highlighting the absence of commercial MNO subscribers. And, as mentioned, such networks could be providing industrial control or replacing enterprise Wi-Fi.

Radio access requires physical hardware, but core network elements can be virtualized and made available in the cloud. Also, circling back to the security advantage for businesses running private 5G networks, enterprise systems will only be visible to authorized user equipment.

Devices belonging to the 5G non-public network will look for a standalone non-public network (SNPN) ID. In contrast, consumer devices latch onto mobile services based on a public land mobile number (PLMN) ID – a combination of a mobile country code and a mobile network code – which is one of the details contained on a handset’s SIM.

Private 5G network starter kit

AWS is trying to bridge the knowledge gap for business users who are thinking about experimenting with private 5G services. The cloud giant has a kit that’s priced based on network traffic rather than the number of connected devices. And, based on the AWS demo video, the setup process is straightforward – comparable to configuring a Wi-Fi network.

Taking a kit-based first step gives firms the chance to run small-scale pilot schemes ahead of making larger investments in mobile infrastructure. And AWS is by no means the only vendor offering easy to navigate solutions. Firecell’s Orion Private 5G dashboard requires no knowledge of a 5G network architecture and configuration.

And the French firm, which aims to democratize private networks and believes in open source as the way forward in telecoms, can supply clients with a rack server, access point, ten pre-configured SIM cards, and one omnidirectional antenna to see how a private 5G network can improve company performance.

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If the idea of private 5G networks sounds appealing and you want to run the numbers on whether it’s an investment that’s worthwhile for your organization, Nokia has made available a 5G business modeling tool. The web application allows users to compare the total cost of ownership of Wi-Fi versus 5G wireless and is based on more than 220+ customer use cases.

Returning to the Frost & Sullivan observations at the top of the story, businesses could end up being the big winners from 5G, as telecoms firms will be highly motivated to tailor their solutions to industrial clients.

“Communications Service Providers (CSPs) have invested significantly in 5G,” emphasizes Morley. “The stark truth is those CSPs depending just on the consumer market for a return on investment will fail.”

The post Is a private 5G network the right choice for your business? appeared first on TechHQ.

It was feared that the economics of generative AI and large language models (LLMs) trained on vast amounts of data gathered from the web and requiring thousands of GPUs could limit the rewards to just a few tech pioneers. But that scenario is changing. More firms are asking what generative AI can do for their business, and the specialist skills that were necessary at the beginning of the boom are being codified to help companies more widely.

AI systems capable of showing the right ads to the right people and enabling more powerful web searches are hugely valuable, which explains why tech giants such as Google, Meta and Microsoft have assigned multi-million dollar budgets to projects in these domains. But that activity barely scratches the surface in terms of potential use cases. And providers are showing that the rise of AI doesn’t just have to benefit massive tech firms with large in-house resources.

Democratizing AI access

Andrew Ng – a famous figure in the success of deep learning – is aware that customization requirements can present a hurdle when it comes to realizing AI’s full potential across the long tail of applications. In principle, vision systems for textile firms and food preparation companies – to give just a couple of use cases – could help millions of workers.

Ng’s vision is that simple-to-use platforms that take the heavy lifting out of building an AI model will empower vast numbers of businesses that have, until now, been unable to reap the benefits. Ng’s team has developed a platform dubbed LandingLens that ‘makes computer vision super easy’. And users can quickly educate the system to automate defect detection, improving product quality and dramatically reducing the need for time-consuming manual inspection.

Considering other streamlining options, generative AI and LLMs, to quote Google, represent the pinnacle of information retrieval technology. And while implementing these models may be child’s play for tech giants, how can other companies – for example, firms without large IT teams and working outside of the tech sector – leverage these huge search gains for their own businesses?

Considering what generative AI can do for business, it’s worth noting that LLMs enable powerful summarization features to complement enterprise search. And search queries can be not just conversational text, but also include images to provide so-called multi-modal capabilities.

Google has launched what it dubs Gen App Builder to make generative AI and LLM capabilities more widely available to developers. The product is in early access and promises to give users an out-of-the-box dev experience, accessed via the Google Cloud console.

How to build an AI-powered search engine for your business

The Gen App Builder can create search engines based on web content, by inputting a series of URLs, or users can specify structured data – for example, files in JSON format or BigQuery sources. And if you’re wanting to rapidly search through hundreds of reports, the platform can handle that too – thanks to an unstructured data option, which can be fed with PDFs.

Once created, these custom generative AI search engines can be integrated into applications using API calls and code snippets such as HTML widgets, which users can embed in their websites. What’s more, the Gen App Builder comes with analytics capabilities that provide a wide range of per-session search metrics.

The power of AI-based search for enterprise comes to light when you consider the differences between how traditional databases index information and the way that deep learning models file their data. Rather than tabulate various attributes, AI systems typically employ embeddings as their data structures. And this is a game changer.

Embedding space – mapping the meaning of content

“Once lined with specific content like text, images, tweets, or anything, AI creates a space called embedding space, which is essentially a map of the contents’ meaning,” explains Kaz Sato, a Developer Advocate at Google.

For example, you could represent a document that contained 20% financial information, 50% technical data, and 30% marketing content as a 3D coordinate (0.2, 0.5, 0.3) in embedding space. And it would then follow that a file with the coordinates (0.18, 0.49, 0.33) may be related based on the similarity of those two sets of numbers.

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What’s more, real-life systems can represent a vast number of signals across many more coordinates. “In reality, embedding space may have hundreds or thousands of dimensions that can represent millions of different categories of the content,” said Sato.

One way of picturing deep learning is to imagine a process that maps high-dimension raw data onto coordinates, which provides meaning and captures the semantics of the inputs. For example, Google itself uses the approach to organize millions of websites, videos, and apps. And, once populated, the semantically related embeddings can be used to recommend related content.

Google’s embedding projector has various examples of how data can appear compressed into three dimensions. And it’s fascinating to see how sentences, despite being written in different languages, populate similar locations when they share the same meaning.

When users key their search queries into Google, the search engine giant converts that sentence into an embedding that allows a fast vector comparison of that intention with pre-transformed web pages. And the technology gives a performance boost over using keywords alone to match search queries with results.

Enterprise search 2.0

Similarly, companies can use embeddings to represent their products and other internal data, which again highlights what generative AI can do for business. Imagine applying the power of a Google search to company data and making that functionality available internally to staff.

It opens the door way beyond searching a product database, which may be out of date or contain errors. Instead, employees could also query multiple PDF catalogs, product images, and a wide variety of company content, all within a single enterprise search.

As mentioned, tools bring synopsis capabilities to enterprises, but based on company information. And this means that staff can attribute a trusted source to the business data, compared with using ChatGPT, which is decidedly more hit-and-miss.

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On TechHQ, we’ve written about how the power of generative AI and LLMs is improving legal front door operations. Firms can use these tools to build advanced chatbots ingested with company data to reply to common staff queries, freeing up legal teams, and other experts to handle new requests from the business.

And platforms such as Grammarly Go are bringing generative AI and LLMs to enterprises, SME’s, and any professionals that work in a team. The recently updated app gives workers the ability to not just autocomplete documents, but include bespoke project terminology and definitions.

Picking up again on the power of semantic search, generative AI and LLMs can dramatically streamline large-scale document preparation. For example, imagine having to respond to an information request that requires removing sensitive details such as PII or unrelated company activity from hundreds of individual documents, spreadsheets, emails, instant messages, and PDFs.

With the right AI system, enterprises can streamline their operations to the point that document preparation is just a few-click process. And there’s a growing number of customizable AI model providers that have solutions designed specifically around the needs of companies.

Today we announced the expansion of our collaboration with @awscloud to extend the availability of our foundational AI models to Amazon Bedrock – a fully managed service that makes pre-trained foundation models accessible via an API.https://t.co/ndeRJCWpan

— cohere (@cohere) July 26, 2023

SambaNova Systems, based in Palo Alto, US, was founded to help business customers thrive in a new era of AI. And its platform is – according to the firm – specifically optimized for enterprises and government organizations. Also, customers retain ownership of models that have been adapted with their data, which points to the importance of AI security and keeping enterprise information in safe hands.

Many firms, worried about company operations being leaked, are telling employees not to use public chatbots at work. External moderators have access to prompts entered into free services such as OpenAI’s hugely popular ChatGPT to monitor that public systems aren’t being manipulated by bad actors. And even if model responses are kept private, the text prompts alone could still reveal much about what businesses were up to if linked to a company email address.

Private LLMs extend what generative AI can do for business

Given concerns about inadvertently distributing business intelligence, it’s no surprise to see a raft of vendors offering private LLMs, fine-tuned on company data and gated for internal use only. The NVIDIA NeMo service is badged as being able to help enterprises combine LLMs with their proprietary data to improve chatbots, customer service operations, and other business functions.

And not only can companies better protect their data using custom services, they can set guardrails so that AI applications don’t go rogue and offer advice beyond the boundary of their digital training foundations.

The post What can generative AI do for business? appeared first on TechHQ.

• The Google Bard AI is chasing ChatGPT’s dominance.
• That holds true in some of the less ethical aspects of how it’s trained.
• Bard trainers are frequently low-wage workers encouraged to do only minimal research.

The Google Bard AI chatbot is making headlines, with new languages added in a bid to steal the limelight from ChatGPT, the first generative AI bot that went viral late last year. Meanwhile, the contractors who trained the chatbot are being pushed out of public view.

Google’s Bard AI provides answers that are well-sourced and evidence-based, thanks to thousands of outside contractors from companies including Appen Ltd. and Accenture Plc.

Bloomberg reported that the contractors are paid as little as $14/hour and labor with minimal training and under frenzied deadlines. Those who have come forward declined to be named, fearing job loss. Despite generative AI being lauded as a harbinger of massive change, chatbots like Bard rely on human workers to review the answers, provide feedback on mistakes, and weed out bias.

After OpenAI’s ChatGPT launched in November 2022, Google made AI a major priority across the company. It rushed to add the technology to its flagship products and in May, at the company’s annual I/O developers conference, Google opened up Bard to 180 countries. It also unveiled experimental AI features in marquee products like search, email, and Google Docs.

According to six current Google contract workers, as the company embarked on its AI race, their workloads and the complexity of the tasks increased. Despite not having the necessary expertise, they were expected to assess answers ranging from medication doses to state laws.

“As it stands right now, people are scared, stressed, underpaid, don’t know what’s going on,” said one of the contractors. “And that culture of fear is not conducive to getting the quality and the teamwork that you want out of all of us.”

High demand, low research, low reward – is generative AI just a chatty sweatshop?

Aside from the ethical question, there are concerns that working conditions will harm the quality of answers that users see on what Google is positioning as public resources in health, education, and everyday life. In May, a Google contract staffer wrote to Congress that the speed at which they are required to review content could lead to Bard becoming a “faulty” and “dangerous” product.

Contractors say they’ve been working on AI-related tasks from as far back as January this year. Workers are frequently asked to determine whether the AI model’s answers contain verifiable evidence. One trainer, employed by Appen, was recently asked to compare two answers providing information about the latest news on Florida’s ban on gender-affirming care, rating the responses by helpfulness and relevance.

The employees training Google Bard AI are assessing high-stakes topics: one of the examples in the instructions talks about evidence a rater could use to determine the right dosages for a medication called Lisinopril, used to treat high blood pressure .

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The guidelines say that surveying the AI’s response for misleading content should be “based on your current knowledge or quick web search… you do not need to perform a rigorous fact check” when assessing answers for helpfulness.

Staff also have to ensure that responses don’t “contain harmful, offensive, or overly sexual content,” and don’t “contain inaccurate, deceptive, or misleading information.” This sounds much like the scandal that OpenAI was involved in after contractors at outsourcing company Sama came forward about the type of work they were expected to do.

Unethical training processes – Google Bard AI and ChatGPT

From WSJ’s podcast series, the Journal heard from Kenyan staff who helped train ChatGPT. The episode episode aired on July 11, entitled The Hidden Workforce that Helped Filter Violence and Abuse Out of ChatGPT.

Initially, the work contractors undertook was relatively straightforward annotation of images and blocks of text, but soon the prompts took a darker turn.

Host Annie Minoff summed up the responsibilities of Sama workers like Alex Cairo as “to read descriptions of extreme violence, rape, suicide, and to categorize those texts for the AI. To train the AI chatbot to refuse to write anything awful, like a description of a child being abused or a method for ending your own life, it first had to know what those topics were.

Counselling is useful, but some things can’t be unseen when training generative AI.

Emily Bender’s Twitter thread on OpenAI’s outsourcing.

According to Karen Hao, “Kenya is a low-income country, and it has a very high unemployment rate. Wages are really low, which is very attractive to tech companies that are trying to increase their profit margins. And it’s also a highly educated workforce that speaks English because of colonization and there’s good Wi-Fi infrastructure.” This is partly why outsourcing is so common for tech companies.

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Outsourcing also ensures companies plausible deniability. Contract staffers training Bard never received any direct contact from Google about AI-related work; it was all filtered through their employer. Workers are worried they’re helping create a bad product; they have no idea where the AI-generated responses they’re seeing come from, or where their feedback goes.

Google released a statement that claimed it “is simply not the employer of any of these workers. Our suppliers, as the employers, determine their working conditions, including pay and benefits, hours and tasks assigned, and employment changes – not Google.”

Ah, the loopholes of subcontracting.

Ed Stackhouse, an Appen worker who sent the letter to Congress in May, said he and other workers appeared to be graded for their work in mysterious, automated ways. They have no way to communicate with Google directly, besides providing feedback in a “comments” entry on each individual task. And they have to move fast. “We’re getting flagged by a type of AI telling us not to take our time on the AI,” Stackhouse added.

Bloomberg saw documents showing convoluted instructions that workers have to apply to tasks with deadlines for auditing answers from Google Bard AI that can be as short as three minutes.

Some of the answers they encounter can be bizarre. In response to the prompt, “Suggest the best words I can make with the letters: k, e, g, a, o, g, w,” one answer generated by the AI listed 43 possible words, starting with suggestion No. 1: “wagon.” Suggestions 2 through 43, meanwhile, repeated the word “WOKE” over and over.

Staffers, who have encountered war footage, bestiality, hate speech and child pornography, do have healthcare benefits: “counselling service” options allow workers to phone a hotline for mental health advice.

As with outsourced Sama staff, originally Accenture workers weren’t handling anything too graphic or demanding. They were asked to write creative responses for Google’s Bard AI project; the job was to file as many creative responses to the prompts as possible each workday.

Training AI models is a “labor exploitation story”

Emily Bender, a professor of computational linguistics at the University of Washington, said the work of these contract staffers at Google and other technology platforms is “a labor exploitation story,” pointing to their precarious job security and how some of these kinds of workers are paid well below a living wage. “Playing with one of these systems, and saying you’re doing it just for fun — maybe it feels less fun if you think about what it’s taken to create and the human impact of that,” Bender said.

The conclusion of Emily Bender’s thread on the OpenAI training scandal.

Bender said it makes little sense for large tech corporations to encourage people to ask an AI chatbot questions on such a broad range of topics, and to be presenting them as “everything machines.”

“Why should the same machine that is able to give you the weather forecast in Florida also be able to give you advice about medication doses?” she asked. “The people behind the machine who are tasked with making it be somewhat less terrible in some of those circumstances have an impossible job.”

Bard – a responsible approach to AI?

The post Questionable ethics behind the training of the Google Bard AI? appeared first on TechHQ.

Being able to recognize numbers and characters was an early big win for machine learning. And one of the first practical lessons for AI students is often training a simple algorithm to classify digits based on the classic EMNIST dataset of 28×28 pixel images of handwritten numbers. Today, optical character recognition (OCR) is big business with a market size in excess of US $11 billion, according to analyst firm Global Data. And high-achieving AI systems include automatic number plate recognition (ANPR) technology, which has become commonplace on road networks worldwide.

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In the UK alone, based on Police data, national ANPR cameras generate in the region of 60 million vehicle number plate read records every day. And, while the importance of ANPR data in crime prevention and law enforcement shouldn’t be overlooked, being able to recognize cars, trucks, and other vehicles has a wide range of applications.

Number plate recognition systems are used by firms to log facilities access and to enable ticketless parking. ANPR data also supports intelligent traffic management systems, helping national highways authorities to accurately calculate journey times and monitor road networks in greater detail.

In Bristol, UK – where TechHQ has its headquarters – 49 ANPR cameras have been installed to deliver a clean air zone (CAZ) in the city center. According to supplier SEA – which won the contract to provide ANPR technology for the CAZ – individual cameras can monitor up to three lanes of traffic. And the computer vision system can retrieve vehicle make, model, gross weight, engine type, Euro rating and CO2 emission band from DVLA databases based on OCR information.

Adding vehicle color recognition to the ANPR mix

But that’s by no means all of the vehicle identifiers that can be gathered using roadside cameras and other image sources, as research published in the Frontiers of Computer Science shows (alternative link). The team, based in China, highlights the opportunity for systems to perform vehicle color recognition (VCR), and there are some good reasons why adding VCR to the ANPR mix is helpful.

Not all drivers are careful to keep their license plates clean, and some may cover, remove, or even – in the case of bad actors – use forged plates, all of which will disrupt the performance of conventional ANPR systems.

The idea of using vehicle color to augment other characteristics acquired using computer vision dates back to an earlier study performed in 2015. In this preliminary work, researchers in Taiwan used decision tree classification to separate road traffic images into a handful of categories based on vehicle color.

They say there are over 250 #AI (ANPR) and red-light violation detection traffic cameras operational in Namma Bengaluru.

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Twitter video appears to show that ANPR cameras can identify helmetless riders at road junctions.

Physical characteristics such as the chemistry, color, number, and layer sequence of paint can all be used to help differentiate one vehicle from another. Since 1997, the FBI and Royal Canadian Mounted Police have teamed up to create a database of more than 9000 paint records dubbed Paint Data Query (PDQ). And the body of information can be used to identify most new vehicles sold in North America after 1973, including models marketed by foreign manufacturers as well as domestic OEMs.

VCR systems can’t match the capabilities of having a physical paint sample to hand, but – as the latest studies show – the number of vehicle colors that can be reliably detected using computer vision is increasing. For example, using a VCR method based on Smooth Modulation Neural Network with Multi-Scale Feature Fusion (SMNN-MSFF), AI experts have almost doubled the number of color categories that can be recognized remotely.

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This latest VCR dataset consists of 10,091 vehicle images extracted from 100 hours of urban road surveillance video. And given the vast amount of traffic footage that is captured on highways globally, this work should be considered as a proof-of-concept rather than the end of the road. The prospects for generating more training data are surely huge.

Based on the results presented in the 2023 paper, VCR is already capable of distinguishing between different shades of vehicle colors – for example, by identifying champagne, yellow, lemon-yellow, and earthy-yellow as distinct categories. And combining VCR with ANPR data adds up to more robust vehicle identification.

As the researchers point out, the fact that vehicles typically only have a single dominant color, which isn’t easily damaged in its entirety or changed– compared with swapping a license plate – makes auxiliary VCR data worth having. And for drivers that don’t want to stand out? White was the most popular vehicle color in the group’s VCR training footage – representing more than a third of the examples in the data set. Also, considering other geographies, over a quarter of cars in the US are painted white, according to iSeeCars.com – a search engine for cars.

Some commercial ANPR systems are already capable of performing VCR, although possibly not with the same fidelity as the neural network used by the research team profiled in this article. Online demos of ANPR systems – which can be viewed on YouTube – show evidence of low-level vehicle color classification, but it’s clear that the software can’t distinguish between different shades. For example, when a dark blue car drives by it’s labeled as simply being blue, and similarly for a different model painted this time in a lighter hue.

DIY ANPR

To understand what’s involved in automatically interpreting number plate information using OCR, it’s instructive to check out some of the many coding tutorials online. Rather than start from scratch, it’s possible to use custom-trained object detection models such as YOLO – included as part of NVIDIA’s TAO Toolkit, designed to speed up the creation of computer vision AI applications – to extract license plate images from footage of vehicles.

Having narrowed down the region(s) of interest, developers can then deploy OCR routines to read out the license plate numbering and lettering and send API calls to an ANPR database to retrieve the corresponding vehicle records. Note that users will need to ensure that they are using this data lawfully. Organizations such as the UK-based Information Commissioner’s Office has guidance on data protection implications of collecting ANPR information.

GDPR and other data privacy laws may apply to people, not vehicles, but that line is easily crossed if ANPR information can be linked to the driver and matched to other PII. And organizations such as the Electronic Frontier Foundation caution that automated license plate reader data can ‘paint an intimate portrait of a driver’s life’.

If playing around with Python libraries and having fun with a Raspberry Pi is starting to look like surveillance, then it’s time to call quits on the coding lesson.

LPR on the Edge

Video analytics firm Azena – a Bosch-funded German tech start-up based in Munich, with international offices in Eindhoven and Pittsburgh – offers edge-based license plate recognition (LPR). And commercial operators wanting to reduce their exposure to data concerns may want to consider self-contained edge-based solutions that minimize information being sent to the cloud.

Azena collaborated with Czech-based FF Group – which offers a range of traffic monitoring capabilities, including a Smart City Starter Kit – on the setup. And the system now features make, model, and color recognition (MMCR) technology. FF Group first started working on make and model recognition, powered by Nvidia hardware, back in 2017. Sampling 500k cars, the algorithm was accurate in 92% of cases, and that was just in early testing.

Products can perform vehicle count, direction detection, LPR, and MMCR of traffic traveling at speeds of up to 120 km/h. The capabilities of modern video analytics are impressive. But at the same time, if you’re looking for somewhere to hide, maybe don’t drive there.

The post AI vehicle color analysis turbocharges automatic number plate recognition (ANPR) appeared first on TechHQ.

The self-organizing behavior of swarm robots is beneficial in many ways, from building smarter tunnels and conducting remote surveys to paving the way for advanced rescue missions. But what happens when one of those robots goes rogue or breaks down? How can the collective intelligence of the swarm fight back against bad actors in the pack? The answer, according to an increasing number of experts, is smart contracts – using the principles of a blockchain token economy to secure robot swarms.

Publishing their results in the latest issue of Science Robotics, researchers based at IRIDIA – the Artificial Intelligence research laboratory of the Université Libre de Bruxelles – have shown how rationing ‘crypto tokens’ can neutralize discrepancies between the intended and actual behavior of swarm robots. “This discrepancy can be a result of programming errors, failed components, or malicious attacks,” writes the team.

Swarm robot security

In a demonstration using 24 Pi-puck robots, which feature Raspberry Pi Zero W hardware, the AI group implemented a ‘proof-of-authority consensus algorithm’ to govern the behavior of the swarm. Robots communicated with each other according to a smart contract – a computer program stored on a blockchain framework. “Each robot owned crypto tokens that it could spend to participate in security-critical swarm activities by sending transactions to the smart contract,” explain the developers.

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Tokens were allocated at exponentially increasing time intervals to all of the swarm robots, much like a universal basic income, which allowed the group to communicate. But the smart contract was set up to only reward members for sending ‘good’ transactions, with no top-up available for undesirable actions. And the reward structure meant that malicious or malfunctioning robots soon ran out of tokens, leaving them without funds to send transactions and unable to influence swarm behavior.

The use of a blockchain token economy to secure robot swarms has been gaining ground for a number of years, with simulations yielding promising results. And the latest results show how such a framework can be applied in the real world.

Demo time: Pi-puck robots use their swarm intelligence to measure white pixels. Bad data is mitigated by using smart contracts to reward transactions that are beneficial to the task. Image credit: V. Strobel, A. Pacheco, & M. Dorigo.

Top 3 benefits of swarm robots –

1. Operating as a large group, swarm robots are capable of complex behavior. Inspiration includes ants, bees, and other creatures that can team up and organize themselves to achieve great feats.
2. Many simple robots are easier and cheaper to design and build compared with coming up with a solution based on a single device.
3. Redundancy and resilience: operations can continue even if some members of the robot swarm suffer mechanical failure or have an electrical fault. And, as we’ve seen, blockchain technology can mitigate against security issues.

The small size of swarm robots allows them to move through dense and otherwise hard-to-navigate areas such as forests, where they can gather ecological information. Engineers in China have shown how 10 flying micro-drones, each weighing less than a full can of Pepsi, can collaboratively survey an outdoor area – even in the presence of moving obstacles and with human interference.

Each individual robot may appear relatively simplistic, but the power of the group acts as a multiplier – an effect often seen in nature. Tim Landgraf – a German researcher who’s been studying bees for coming up to 20 years – notes that while an individual bee has a brain the size of a pinhead, the insects can navigate a radius of up to 6km around their hives. “They act collectively as a network of tiny brains linked together through various forms of communication,” he comments. “They are sharing information, but also energy.”

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The power of swarm intelligence inspired Landgraf to co-found the Dahlem Center for Machine Learning and Robotics in Berlin, where he and his colleagues apply bee-smarts to solve technology roadblocks in self-driving cars and other industrial sectors. And nature has much to teach device-makers about how to improve their products.

Ants can build dynamic bridges out of themselves to allow other ants to cross gaps. What’s more, the assemblies can compensate for movements in the foundations if the structure happens to be built on unstable materials such as leaves. And this hints at some of the applications for swarm robots – complex and harsh terrains.

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— UKRobotics (@UKRobotics) June 19, 2023

Swarm robots have huge potential in rescue operations. On TechHQ we’ve written about how chemical-sensing smart dust could travel large distances to register signs of life. But robot swarms can assist in other ways too. Radhika Nagpal, head of the Self-Organizing Systems Research Group at Harvard University, US, points out that swarm robots could potentially help to shore up a collapsing structure by building a supporting framework out of their bodies.

In construction, swarm robots provide the opportunity to build tunnels from the outside in. And again, looking at nature, termites have long proven to be expert tunnel builders, using their own saliva to bind soil and wood to reinforce their structures. UK-based swarm robotics operator hyperTunnel – which lists NetworkRail as a strategic partner – has its own take on this concept by injecting ground-improving chemistry to 3D print tunnel supports in-situ.

Data forensics

Returning to the application of blockchain technology to swarm robotics, it’s worth mentioning that having a digital ledger confers other rewards too. “During the autonomous operation of the robot swarm, a conflict-free logbook of the messages exchanged is automatically stored in the blockchain,” reports the IRIDIA team. “This logbook could be used for establishing accountability and for performing postliminary investigation and data forensics because the blockchain offers non-repudiation.”

Demonstrating how security frameworks keep robots accountable for their actions will help to build trust in the use of self-organizing swarms of devices. There’s also risk versus reward to consider. Having robot swarms carry out search and rescue missions, holding up damaged buildings to prevent further loss of life in environments that are too dangerous for humans to enter, is unlikely to meet with many objections.

The post Blockchain token economy secures swarm robots appeared first on TechHQ.

• Online verification is evolving into new areas.
• Specialized apps and operators are helping delivering online verification.
• Bad actors are evolving their fraudulent techniques too.

Identity verification is critical to ensuring the security and longevity of companies across all sectors. As business moves online, verification methods must follow suit to prevent fraud and meet compliance regulations. Not only is it important that a customer can be identified correctly, but you can manage front-end risk to your company by verifying vendor identities too.

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In the online landscape, risks are mounting. When physical documentation was the main way of verifying identity, there were fewer ways for criminals to intercede. Now, every technological advance brings new threats.

More risk avenues mean more verification methods are needed. Online, there’s more chance that a document will be fraudulent, so organizations are expected to take extra steps to verify customer and client identities.

It’s unlikely, however, that a company could carry out verification processes without a little help. There’s a huge market for identity verification platforms out there that use additional personal information beyond a username and password to verify the identity of an individual.

This additional information comes in various forms, including date of birth, government-issued IDs, voice recognition, biometrics, and even facial recognition.

Knowledge-based authentication is a common way to prevent fraudulent logins. After inputting their password, a customer is asked a security question that should have an easy to remember, hard to guess answer. The method is increasingly outdated, though, what with our pathological need to post everything – first pets, mother’s maiden name – on social media.

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It’s important that when users sign up to a service, the account they use isn’t going to be spam. To check this, two-factor authentication should be used, proving that a user has access to the inbox of whatever contact method they provide on sign-up.

To enhance and ease this, Okta provides cloud software that helps companies manage and secure user authentication into applications, and helps developers build identity controls into applications, web services and devices.

Identity verification also protects a business by verifying new employees. This tends to be about checking documents, which is something that technology makes faster and easier, and reduces internal instances of identity theft, fraudulent transactions, and potential unauthorized access to an organization’s information.

Online verification methods

Primarily, identifying an individual determines whether a government-issues ID relates to the user. Often, to make such identification possible, a customer or employee must provide a photo or video of themselves holding a form of ID.

Although human verification is an option, international companies might waste hours dealing with regional documentation. Trustmatic automates trust between businesses and their customers. Its onboarding process has a first-time completion rate of 98% and the platform supports a variety of identity documents from over 248 countries and territories, along with 138 languages.

More and more companies are helping provide online authentication and verification.

Ondato does this type of live document verification. It integrates via a SaaS business model and offers modules on biometrics, e-signature and compliance management tools. Similarly, Token of Trust uses multiple ID verification types. The company offers government ID verification, electronic ID verification, document scanning, biometrics and age address or social verification.

Biometrics once seemed reserved for hi-tech spy movies, but now almost every smartphone user has their identity checked with biometrics every time they unlock their phone. Another buzzword that feels like it was only recently coined is “digital footprint.” As social media fearmongering has given way to AI worries, the focus on what information your digital footprint holds has diminished.

Ever unlock a phone with a fingerprint? Welcome to biometric verification.

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However, for security checks, digital footprint analysis looks at the unique data that every individual accrues online. Relevant information includes IP fraud scoring, email and phone analysis, reverse social and digital platform lookup, PEP, blacklists, sanctions lists and crime watchlists. All of these things create a user profile.

Your digital footprint can still identify you.

In this way, not only can you verify an identity, but the risk/value associated with the customer. SEON uses digital footprint analysis, feeding data points through customizable risk rules. High-value customers are identified using alternative data as an extra step before Know Your Customer (KYC) checks are done.

KYC is a critical function to assess customer risk and a legal requirement to comply with Anti-Money Laundering (AML) laws. Effective KYC involves knowing a customer’s identity, their financial activities and the risk they pose.

KYC is particularly important for financial institutions. Doing so involves multiple processes, so a software that automates everything will free up valuable time. ComplyCube automates AML and KYC compliance for businesses. It also enables verification of customer details through ID docs, biometrics, and government databases.

There are always going to be those who are cagier about identification documents, maybe with good reason. To that end, ID.me simplifies how individuals prove and share their identity online, meaning that the verification process is carried out by consumers before there’s any business interaction.

ID.me provides both video chat and in-person verification, increasing access and equity. The team is committed to “No Identity Left Behind” to enable all users to have a secure digital identity.

Whether we like it or not, identity verification is as online as the rest of society, and being able to prove you are who you say you are is critical to existing in modern society. Businesses are legally required to carry out these checks on their customers and potential partners.

Bad actors stay on top of the latest technology to exploit new loopholes and oversights. As such, security systems need to do the same. The Center for Identification Technology Research (CITeR) addresses research challenges related to securing individual identity in a global society with a focus on automated biometric recognition and credibility assessment.

CITeR carries out research in emerging enabling technologies; interdisciplinary training of scientists and engineers; and facilitation of technology transfer to the private and government sectors.

Science fiction becomes mundane fact if you wait long enough. Movement analysis as a factor in multifactor authentication is already in development.

The post Online ID checks, biometrics and more appeared first on TechHQ.

• Generative AI is being used in healthcare settings already.
• Without nuance, it has been shown to go badly wrong.
• Nevertheless, the potential for AI in the future of healthcare is enormous.

The future of healthcare technology, as science fiction has occasionally fed it to us, feels like it’s drawing closer than it’s ever been.

But it’s not all plain sailing from here to cancer-zapping nanobots, robo-nurses, and medical tricorders. If we take a look at some of the latest ways in which tech has been used in the medical sector, we’ll see pros and cons, both already in the field and just around the corner.

Medicine might seem like an area that should be practiced only by well-trained humans. When we put our lives in their hands, after all, we understand that humans have a duty of care – and have been trained to perform it.

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However, technology is responsible for huge medical advances, and it only makes sense that the tech world’s latest darling – artificial intelligence – is being trialled in the field.

Doctors, hospital executives and data scientists (put them in a bar and you’re halfway to a joke) all agree that artificial intelligence could help solve huge healthcare problems.

It’s been used already, when the Mayo Clinic combined AI and machine learning with clinical practice to improve care, applying the technology to radiology. You can watch a panel discussion about AI Adoption for Clinical Practice during the Mayo Clinic Platform Conference 2022 here.

Unfortunately, healthcare systems are biased, and the new tools could also perpetuate long-standing racial inequities in the delivery of healthcare. Having been trained on historical records, there’s scope for the same patterns to continue.

“If you mess this up, you can really, really harm people by entrenching systemic racism further into the health system,” said Dr. Mark Sendak, a lead data scientist at the Duke Institute for Health Innovation.

Healthcare organization Carbon Health has introduced an AI tool to generate medical records automatically. With a patient’s consent, meetings with the doctor will be recorded and the audio sent to Amazon’s AWS Transcribe Medical cloud service, which transcribes it.

The transcript – along with data from the patient’s medical records, including recent test results – is passed to an ML model that produces notes summarizing important information gathered in the consultation.

Company CEO Eren Bali said the software is directly integrated into the firm’s electronic health records (EHR) system and is powered by OpenAI’s latest language model, GPT-4.

“The use of scribes and transcription services is standard in the healthcare industry, and a majority of patients provide consent to have their visit recorded by their provider,” a spokesperson told The Register on Monday.

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The AI-generated text will still have to be reviewed by physicians, although Carbon Health claims that 88% of the information can be accepted without edits.

Theoretically, the tool will increase the number of patients that the doctor’s office can see, as the tool can produce consultation summaries in four minutes compared to the 16 it typically takes a human.

It’ll also cost less than a human doctor.

The future of healthcare technology: faster and cheaper

Last week the National Eating Disorders Association (NEDA) announced a new chatbot feature on its helpline, named Tessa. The entire human staff, six paid workers and around 200 volunteers were to be replaced by the chatbot.

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Less than a week later, Monday, June 5, the chatbot was pulled from the site. An eating disorder activist made an Instagram post, sounding the alarm that the chatbot wasn’t helpful – in fact it was actively dangerous to sufferers.

Despite stating that she had an eating disorder, Maxwell received weight loss tips from the helpline. Initially, NEDA pushed back against the claims in its own Instagram post – that was deleted soon after Maxwell provided screenshots as proof.

While anyone can make an honest mistake, that initial pushback is symptomatic of the sometimes blind faith people are already putting in systems that use generative AI.

“It came to our attention [Monday] night that the current version of the Tessa chatbot, running the Body Positive program, may have given information that was harmful,” NEDA said in an Instagram post. “We are investigating this immediately and have taken down that program until further notice for a complete investigation.”

Vice President Lauren Smolar denied that the move to AI came from the hotline staff’s threat of unionization. She told NPR that the organization was concerned about how to keep up with the demand from the increasing number of calls and long wait times – last year staff took nearly 70,000 calls.

She also stated that NEDA never intended the automated chat function to completely replace the human-powered call line (contradicting the fact of the staff’s literal replacement).

Whether Tessa was a techno-union buster or not, technology is becoming a convenient scapegoat when things go wrong.

The AI equivalent of “The robot dog ate my homework” is becoming ever more commonplace as an explanation – helping re-define what people can expect from medical services.

A California-based company that sells a blood test kit which detects cancer has said it incorrectly informed roughly 400 customers that they might have cancer.

Coming in at $949, the Galleri test by Grail detects a marker for more than 50 types of cancer. Customers who paid out hard-earned money received a letter “stating incorrectly that a cancer signal was detected,” a spokeswoman told CBS MoneyWatch.  

The error was supposedly the fault of the vendor, PWN Health, and was put down to a “software configuration issue.” The point being that at that price – and with the potential terror and stress of a cancer diagnosis on the other side of it – the company should have a legal duty to ensure its software is configured correctly.

In a statement, PWN Health said the problem was down to “a misconfiguration of our patient engagement platform used to send templated communications to individuals.”

The robot dog ate my homework. Wrongly.

It also claimed that it has added processes to make sure such a mistake wouldn’t occur again, and started contacting the people who received the erroneous letters within 36 hours.

“The issue was in no way related to or caused by an incorrect Galleri laboratory test result.”

There’s no risk that the medical profession is going to be taken over by technology anytime soon, but while technological failures make headlines, it’s worth remembering the successes that are happening every day.

The future of healthcare technology isn’t exactly hanging in the balance – it’s here already.

No-one would argue against technology being put to medical use, but even as AI gets more capable, we should be questioning which roles we want it to take on.

Plus, in the name of preventing its heavily forecast world domination, maybe AI shouldn’t be privy to our innermost thoughts (or perhaps our physical weak points) just yet.

We may be some way from emergency medical holograms just yet – but AI in our medical profession is already here, for better and worse.

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