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Generative AI could play bigger role in new tools from Amazon

Generative AI could play bigger role in new tools from Amazon web services



Generative AI could play bigger role in new tools from Amazon

Generative Artificial Intelligence dominated Amazon Web Services’ re:Invent 2023 conference in Las Vegas, as the company rolled out a slew of new tools for developers and businesses.

Most of what Amazon Web Services (AWS) does isn’t always visible on the consumer side, but that doesn’t mean it isn’t playing an impactful role. It often does for brands that use its cloud and software services, many of whom are familiar brands people interact with in some form or another. ChatGPT almost single-handedly brought AI into the consciousness of the masses, opening the door to everyone to try their hand at typing in prompts and seeing the results.

re:Invent is more of a business-to-business conference, but in the end, much of the show’s content could eventually land right into consumers’ laps. Here are some of the standouts.

AI playing a bigger role in sports

Formula 1 AI booth scaled

Formula 1 (F1), which had just finished its first Grand Prix in Las Vegas, says it will continue to utilize AI tools to improve both driver safety and fan experiences to grow the sport.

If you’re a sports purist, you may not like the role analytics play in scouting and player development, but there’s no question Generative AI is going to be part of various sports’ operations going forward. The PGA is taking a serious stab at it through its own virtual assistant, which it demoed as a proof of concept it wants to put into action in mid-2024.

The idea is actually pretty simple. The AI powered assistant would work in the PGA app or website, where users can ask it to pull up any stat or reference to a player, course, tournament — basically anything involving the PGA Tour going back to 2005. Why only 2005 and not earlier? That’s apparently coming, but for now, the virtual assistant will only pull from data the Tour owns and can index.

Anthropic Chess Robots scaled

Two robots play a game of chess using Anthropic’s platform, countering each other’s moves without human decision-making.

In a demo taking place at the TPC Summerlin golf course that hosts the Shriners Children’s Open during the PGA Tour, pro golfer Tony Finau hit three approach shots (the longest of which was an astounding 384 yards). All data related to each shot appeared on a screen, like distance, carry, ball speed and club speed, among others. Using the virtual assistant, you could ask who holds the record for the longest shot at the same course, or whether a player like Finau has set any records himself. The assistant then pulls up stats and resources, including video clips specific to the query.

In other words, not only can you geek out on the metrics, but also easily reference and present the exact video clip without moving over to YouTube to find it. There are 160,000 hours of video in the library to start, plus the countless stats and data points being fed into the AI assistant.

Anthropic Chess Gen AI scaled

A screen shows every move the robots make, but more importantly, text input describes how each robot rationalized the move.

The PGA is doing this using Amazon Bedrock, a machine learning service to build Generative AI applications, and built the assistant with help from the AWS Generative AI Innovation Centre. It works with any query that falls within the 2005-present window, enabling fans to get answers in real-time without switching apps or losing sight of the live action. For now, it will mainly focus on the men’s tour, as it will take time to apply it to the women’s tour because it doesn’t have the same level of data. Scott Gutterman, senior vice president of digital operations for the PGA Tour, told MobileSyrup that building a proprietary AI model takes longer to do, so launching the virtual assistant to incorporate more data will likely come in a series of private betas.

Gutterman also noted that Generative AI could change how players assess their own performance, like which shots or holes cost the most strokes in a tournament or entire year and why they were so costly. AI could also pull data to compare with other players to better understand how the same search criteria applies to others. It sounds like Moneyball on another level, but Gutterman also clarified that many players already use tracking analytics to learn the subtleties of their play and the various courses they play on to improve their game.

Amazon Q

2023 amazon q business introSpeaking of assistants, Amazon unveiled its own AI-powered chatbot for businesses. The idea is to take 17 years of AWS data to power Q in helping businesses figure out what to do in a given situation. That could be a new solution or improving the system in some way. Q basically has everything AWS learned since it launched in 2006, parsing the data to provide helpful tips or suggestions for any organization using it. For the business itself, it would work by “understanding your systems, your data repositories and your operations,” which would mean indexing all the data and content within the organization to help the AI-powered chatbot make those meaningful suggestions.

With everything it learns, Q could theoretically highlight which aspects of the business need some improvement, or present solutions that involve software the business uses or not. That would also include pointing out inefficiencies in software processes that take too long, offering options on how to cut that down. Since it’s an AWS product, it could also make connections with other vendors who could potentially help as suppliers or providers. Like ChatGPT, Q will also recognize uploaded files, be it text documents, spreadsheets, etc., to summarize the content or find pertinent details upon request.

Amazon claims Q is scalable so that smaller businesses could also use it to help cut costs and scale up in more manageable ways. It’s unclear whether Q will be as effective or reliable as the company claims, but it believes the inherent guardrails put into it will make it easier to trust when it finds something to deal with. It may also only be a matter of time before we see Q power consumer-facing chatbots as well. The jury’s out in any case, so we’ll have to wait and see.

Building apps for dummies with PartyRock

Amaozn PartyRock screenIf you have no idea how to code anything, yet like the prospect of building an app, AWS PartyRock makes it feel like an experiment that won’t go terribly wrong. It’s based on Bedrock and uses a large language model (LLM) to help users build small apps with a non-code template system and user-friendly interface driven by Generative AI at every stage. It’s available to try for free, and while it may look a little weird at first, text prompts largely guide you the whole way. Plus, you can also try it on a phone or tablet, not just on a computer.

If you know your way around some programming language, you can opt for more manual choices, like connecting modules together and determining what happens when they do with your own input. The gist is Amazon hopes the tools in PartyRock help people get past any initial trepidation or hesitancy in trying to build applications. The company often mentioned the importance of education in the growth of Generative AI to prepare for how it may change jobs or create new ones.

Don’t get the wrong idea — you won’t be building the next killer app with the web-based tool noted here, but it could be a stepping stone to learning how to be an app developer without any prior knowledge. At least Amazon hopes that will be the case, so that mini-apps the likes of “Find the Perfect Pup” or “ChatRPG” spur further creative ideas that wouldn’t otherwise disappear in the ether.

Amazon now has an AI image generator

2023 amazon titan image iguana 1Bedrock is also powering Titan Image Generator, giving Amazon’s enterprise customers a tool to produce images through a text-based model based on AI. The premise is that businesses who use it can create images that add to existing subjects, so theoretically, you could have the same exact product in multiple images but in different settings or backgrounds. It can also produce entirely original images based on whatever natural language prompts are applied.

It’s not yet clear how this might affect photographers working with corporate clients, but the Titan Image Generator will be out of consumers’ hands and fall squarely into business use cases. For the time being, Amazon doesn’t intend to make it a competitor to popular text-based image generators like Dall-E and Midjourney. Plus, it’s not even a separate app since it’s embedded in the developer tools AWS offers.

The company also says it wants to safeguard against copyright infringement and inappropriate content by putting up guardrails

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5 Ways Artificial Intelligence is showing Promise as a Decision Maker

CIOs are seeing payoffs from using Artificial Intelligence to automate myriad types of business tasks and workflows



5 ways AI is showing promise as a decision-maker

Artificial Intelligence holds immense promise not just in automating tasks but taking actions that deliver results.

CIOs and others in the C-suite are already seeing payoffs from using AI to automate myriad types of business tasks and workflows. Now they’re eyeing a next-phase opportunity—relying on machine intelligence to handle complex decisions.

“If you look at the advances we have seen in Artificial Intelligence, with the large amounts of data that large language models can process, we can safely hand off various decisions to machines,” says Prasad Ramakrishnan, CIO & SVP of IT at Freshworks.

AI is becoming an integral part of decision-making for many different business functions – from finance to manufacturing to sales. Here’s a look at a few areas where it’s gaining influence.

Chatbot conversations and decisions

By some estimates, intelligent chatbots can already answer 80% of routine customer questions. This reduces costs while improving customer experience. Instead of waiting on hold or navigating through phone menus, customers can instantly get answers from a virtual agent that is far more engaging and knowledgeable than past generations of chatbots.

“Chatbots can come to your rescue with an answer derived from a knowledge base and know what type of tone to use when responding,” says Ramakrishnan.

Companies are now moving toward AI-powered decision-making in customer service—tapping into voice and sentiment analysis to automate complex processes such as recognizing customer intent and taking a recommended action to resolve it.

Sales optimization

In sales, AI can provide account reps with the information they need to close deals. An AI system can gather data from customer relationship management software, social media profiles, email interactions, and purchase histories to identify the candidates most likely to convert.

It can also factor in data specific to a sales prospect, such as whether the person has downloaded a resource or engaged with a particular email message. AI can then guide sales reps to follow up on the most promising prospects.

“It can even feed into the sales narrative, prompting the rep to ask the right questions or use offers that have a higher propensity to appeal to a particular customer,” Ramakrishnan says.

Outcomes are fed back into machine learning models to improve prediction accuracy continually.

Dynamic pricing

Airlines, ride-sharing services, and online retailers have long used dynamic pricing to adjust to changing market conditions. Utilities are an advanced use case: Power companies use sophisticated algorithms to set prices dynamically according to the volume of electricity generated by renewable energy sources and demand at different times of the day.

AI makes this capability available to any business. For example, a retailer could adjust prices on its website based on the visitor’s identity, inventory levels, and competitor prices. Hotels could dynamically adjust room rates based on traffic forecasts, weather conditions, and events in the area.

Supply chain logistics

Optimizing supply chains is a daunting task because of the number of variables involved. AI can help every step of the way. AI-generated “digital twins,” or virtual representations of physical assets or systems, can replicate live scenarios and predict breakdowns.

AI analytics tools can assess supplier performance and capabilities to help companies choose the most reliable sources at the lowest cost; they can further streamline operations by using blockchain technology to execute smart contracts, in which transactions are automatically triggered when certain conditions are met.

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AI & Augmented Decision Intelligence: The Next Paradigm Shift in Hospitality Revenue Management



ai augmented decision making

Artificial Intelligence is everywhere. It’s making inroads into practically every field of human activity. It’s integrated into search engines, word processors, and various apps likely installed on your smartphone in your pocket. And there’s a reason for the tech’s proliferation: it is incredibly adept at analyzing data much faster and at a much larger scale.

In the hospitality realm, most people consider revenue management to be something that only humans can do effectively. Now, with the advent of AI, that’s no longer the case. As technology continues to advance, there is a growing trend toward enhancing, automating, and optimizing decision-making processes, even for complex and collaborative scenarios. This shift reflects the ongoing integration of AI technology to make decisions more efficient, accurate, and streamlined.

Indeed, a growing number of hotels and hotel chains have been looking to reap the rewards of an AI-driven revenue management system (RMS) to optimize their pricing strategies. However, just because a system bears an AI moniker in its marketing doesn’t suddenly turn it into a revenue management silver bullet. The truth is that legacy solutions have algorithms that were created before AI made its appearance, so they can’t be AI-first. These days, that’s a costly mistake.

Hoteliers should recognize the distinctions between a revenue management system just using buzzwords like AI and what a truly modern AI-first system will do when it’s well designed. FLYR’s solution is also designed to be user-friendly and intuitive. It presents complex data in a simple, easy-to-understand format, enabling hoteliers to make informed decisions quickly and efficiently.

We’ll look at why FLYR’s AI-first solution leads the RMS pack with its decision intelligence capabilities in a little more detail, but first, let’s take a deeper look at what decision intelligence is.

What is Decision Intelligence?

Decision intelligence (DI) is a multidisciplinary field that uses advanced techniques from data science, machine learning, and artificial intelligence to guide decision-making processes. In the context of hotel revenue management systems, DI can bring about a transformative change by enabling more accurate, data-driven decisions.

In the realm of dynamic pricing, DI utilizes comprehensive analyses of booking patterns, market trends, and competitor pricing to optimize rates strategically for maximum revenue. Its impact extends to enhancing demand forecasting and providing unparalleled precision for inventory control and staffing decisions.

DI refines guest segmentation by delving into data, allowing personalized services that elevate satisfaction and boost revenue. Furthermore, it guides strategic choices in distribution channel management, optimizing focus for revenue maximization. In addressing the perennial challenge of overbooking, DI’s predictive capabilities empower informed decision-making, minimizing the risks of guest disappointment.

Moving beyond conventional revenue management systems that offer decision support solely based on supply and demand cues, decision intelligence algorithms and models meticulously examine data to grasp price sensitivity. They can autonomously modify rates for specific rooms, nights, and lead times, providing a more dynamic and responsive approach.

Unlike other systems, FLYR algorithms are totally prescriptive, providing insights that would have otherwise flown under the radar – and delivering recommendations for sound decision-making to respond to data-based signals rapidly.

Historical vs. Real-time

A lot of customers of other “AI” RMS providers we talk to describe the challenges around the importance those systems place in historical data. These systems rely on historical data as the basis for their pricing and inventory recommendations, limiting the scope of those recommendations to past patterns and trends. This is less than ideal in dynamic markets prone to rapid shifts – historical data can lose its relevance rather quickly.

The algorithms used in decision intelligence continually analyze your hotel’s stream of data. They can make micro-targeted adjustments on room nights, providing an edge to revenue managers who can quickly respond to shifts in market conditions, which translates to enhanced profitability. That’s no small benefit when demand patterns fluctuate rapidly, such as in the hospitality industry.

In many of the older RMS systems, they typically have to run for a minimum of three months in order to generate useful recommendations. You really don’t get optimized results until you’ve been running the system for a year. In a next-generation system like FLYR, you can start getting optimized pricing and forecasting results as soon as a week after the system is connected. This is what differentiates the FLYR technology and is a true indication that it just works in a new and better way.

Automation & Scalability

With a fully automated pipeline, decision intelligence-capable systems are quite simply more efficient and less error-prone than legacy systems. The latter typically rely on rule-based approaches and manual analysis to spawn their inventory and pricing recommendations. Again, this slows down the process, and the resulting insights may well be out of date. And we all know that human intervention can introduce errors in the data, resulting in errors in the recommendations.

Case in point: a McKinsey study found that an AI-driven RMS could reduce errors by 20% to 50% and reduce lost sales by up to 65% – not a fringe benefit by any measure.

You may have noticed my use of the word “recommendation” when discussing other revenue management systems. That’s because these solutions typically provide recommendations rather than actionable insights. They make suggestions, but the final decision-making will hinge on human judgment, leading to the potential introduction of errors and biases.

They also struggle to handle the sheer volume and complexity of data available today. In contrast, FLYR’s solution automates these processes and uses AI to analyze data more accurately and efficiently. It is also scalable enough to grow your business without skipping a beat. The bigger your business, the more data it generates. So you’re going to want a system that won’t balk under pressure.

Other systems tend to be developed for specific hotel sizes or types and cannot easily accommodate changes in business needs or market conditions. FLYR’s solution is more adaptable and responsive to changes in the market. It continuously learns and adjusts its predictions and recommendations based on new data, ensuring that hotels are always equipped with the most up-to-date and accurate information. This is a significant advantage in the fast-paced and unpredictable hospitality industry.

Integrated vs. Siloed

As mentioned above, decision intelligence operates holistically. That means it integrates, among other metrics, booking patterns, market demand, competitor pricing, and guest preferences. So, it will require access to existing systems for optimal performance and to yield the maximum benefits. FLYR’s decision intelligence-enabled RMS is designed for easy, seamless integration with a plethora of existing systems. This enables hotels to leverage data from various sources in real-time, leading to more informed revenue and guest relationship management decisions.

With limited integration capabilities, legacy systems can’t consider many complex and interrelated factors that significantly impact revenue management. They cannot factor in external data points, like market demand, competitor pricing, and customer segmentation, limiting the insights they can produce.

Leveraging Decision Intelligence

FLYR brings together AI and intuitive workflows to deliver the first-of-its-kind AI-augmented decision intelligence platform. This is a paradigm shift for the hospitality industry, providing massive accuracy, efficiency, and profitability gains over legacy RMS systems.

Offering a trifecta of advantages, decision intelligence enhances predictive insights, optimizes pricing strategies, and refines guest segmentation. Recognizing that the effectiveness of any algorithm hinges on the quality of the input data, as the principle “garbage in, garbage out” aptly underscores, FLYR places a paramount emphasis on obtaining accurate and high-quality data, aiming for a standard of precision that surpasses previous benchmarks.

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Huawei Cloud: Accelerating Intelligence in Europe, for Europe

EMEA organisations expect cloud computing to improve productivity and expedite new product development



Huawei Cloud: Accelerating intelligence in Europe, for Europe

The Digital Future in Europe

The future of European organisations and industries is looking increasingly digital and cloud-centric.  According to Foundry’s Digital Business Study 2023, 91% of EMEA organisations have adopted or plan to adopt a digital-first business strategy. At the same time, Foundry’s Cloud Computing Study 2023 found that 71% of EMEA organisations are defaulting to cloud-based services when upgrading or purchasing, while 60% have been accelerating their cloud migrations over the past year.

EMEA organisations expect cloud computing to improve productivity, enable innovation and new product/service development, and upgrade legacy tech with lower total cost of ownership. 54% of IT Decision Makers also expect to use cloud capabilities to leverage Artificial Intelligence (AI)/Machine Learning (ML) over the next year, which many see as a potential game changer for their industries.

However, EMEA organisations are facing challenges in their digital and cloud journeys. 34% reported a lack of the right skill sets, while 29% cited complexity of IT infrastructure as a major hurdle. There were also concerns around data privacy and security, cloud costs, and compliance issues which would need to be addressed before organisations could reap the potential offered by the cloud.

Enabling Industries through Everything as a Service

To help European industries and organisations overcome these challenges and quickly enable cloud initiatives, Huawei Cloud has adopted a strategy of “Everything as a Service.” This comprises multiple tenets:

  • Infrastructure as a Service allows industry partners to rapidly scale their computing, network and storage across Huawei’s global access network, comprising 30 Regions and 84 Availability Zones across more than 170 countries
  • Technology as a Service makes available Huawei’s continuous innovation and leading-edge capabilities in cloud-native, Artificial Intelligence (AI) and data and media, offering organisations more flexibility and choice in their technology partnerships
  • Expertise as a Service taps on Huawei’s three decades of experience in ICT, as well as the best practices of its network of global partners

Leading innovation in cloud-native, data, and AI

Huawei’s answer to the growing demand for cloud-native as the platform-of-choice for digitalisation is KooVerse: Huawei Cloud’s distributed infrastructure of storage, computing, networking, and security resources, designed with a unified architecture on a globally accessible platform to help organisations leverage the latest in cloud-native services and technologies.

To that point, Huawei is the only founding member of the Cloud-Native Computing Foundation (CNCF) from Asia, due to its consistent investment in developing and improving cloud-native services such as Ubiquitous Cloud-Native Services (UCS), Cloud Container Engine (CCE) Turbo, Cloud Container Instance (CCI), and CCE AutoPilot. In the Netherlands, these services helped 433, the world’s biggest football community, improve production efficiency and reduce costs by 25%.

On the data side, GuassDB is the result of 20 years of experience developing databases. Huawei’s next-generation distributed cloud database is designed for high availability and security, performance and flexibility. GaussDB has been widely used in banking, insurance, securities, and energy.

Tailoring AI for Industries

Building on its “AI for Industries” strategy, Huawei Cloud designed Pangu pre-trained AI models, tailored for a range of industries including finance, government, manufacturing, mining, meteorology, and railways.

“Huawei Cloud Pangu models will empower everyone from every industry with an intelligent assistant,” says Zhang Ping’an, Huawei’s Executive Director and CEO of Huawei Cloud, “and help to reshape all industries with AI.”

In the field of meteorology, the Pangu weather model has become the first to achieve better precision than state-of-the-art numerical weather prediction methods, with a prediction speed several orders of magnitude faster. Consider how the Pangu model was able to predict a 10-day typhoon trajectory in 10 seconds against five hours using current methods. These ground-breaking results were published in the July edition of the scientific journal Nature. In pharmaceutical R&D, the Pangu drug molecule model has drastically shortened lead compound discovery cycles from years to a single month, while achieving a 70% reduction of costs.

Building Trust in the Cloud

Addressing a key concern for EMEA IT leaders, Huawei Cloud has more than 120 security and compliance certifications, including C5 certification with zero deviation. Huawei Cloud provides compliance consulting and security services, while Huawei’s Compliance Compass platform helps users ensure security and compliance.

To help build trust in cloud services in the EU, Huawei Cloud works with SCOPE Europe and EU Cloud CoC to contribute to the Third Country Transfers Module and GDPR Compliance Standards. Huawei Cloud has also released white papers to share its expertise on privacy protection, data security and cloud security, and is a Board Member of the EU Cloud Code of Conduct General Assembly.

Huawei Cloud sees stability and reliability as critical lifelines. Deterministic Site Reliability Engineering (SRE) offers service availability of over 99.99%, with recovery four times faster than industry average. In addition, Huawei Cloud offers a three-level support system, deploying five service teams in Europe, and a series of 24/7 support centers worldwide. 

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