In their infancy, NFTs have suffered mightily from both misconceptions about the technology behind them and manipulation by fraudsters and hackers. Issues have arisen because many people, including creators, rushed to treat them as monetized cryptocurrency projects, tradable assets, and get-rich-fast investments. The more thoughtful, functional opportunities possible with them have often been overshadowed by hype, speculation, speed, and greed.

That’s finally starting to change — and it isn’t because brands made them collectibles or high-end coupons. It’s because evolving technical capabilities are steadily improving NFT functionality.

It’s still true that an NFT refers to an asset, most often an image, tokenized with a unique digital signature recorded on a decentralized, immutable blockchain like the Ethereum network. This signature on a distributed blockchain ledger, across thousands of computers worldwide, ensures that the NFT’s existence and transfer can be verified with an extremely high level of certainty — without relying on a central authority like a bank or broker. That’s well and good, but the public has largely remained unable to see this in practice, translate what it means, or find relevant applications to their lives and work.

With the emergence of programmable NFTs in 2023, we can now directly embed content and executable programs inside an NFT, which creates a significant difference in how we experience them, their utility, and even the jobs they can perform for us. My team’s new Immutable Miniverse Format (IMF), for example, immutably encodes — and can encrypt — tailored messages, games, music, binaries, and interactive experiences within an NFT, visualized as a string of secret code in motion. Importantly, this self-contained “miniverse” is embedded as part of the NFT artwork — without the technology burden of maintaining Web3 access points when being used on traditional social media.

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The Next Level with Generative AI

Fusing NFT art and programming code in a self-contained way is a significant development.

Programmable NFTs can be leveraged for work and play, shared on today’s social media platforms (Web2) and even serve as the building blocks of a kind of “app store” for Web3 and a more decentralized economy, comparable to today’s mobile phone apps. Additionally, the use cases in everyone’s mind involve generative AI technologies.

For good reason: the ultimate NFT utility may be the capacity to tokenize intelligence in a decentralized and immutable fashion.

Here, we’re not talking about using generative AI to churn out NFT art, rather the focus is on integrating LLM chat capabilities into programmable NFTs and ultimately empowering owners to program and mint their own NFTs to do this. My team is enabling NFT holders to have a private conversation with and within their NFT about the domain and knowledge embedded inside their NFT’s miniverse. This means leveraging a customized, fine-tuned version of an LLM, while not subjecting any input to third-party services that could opaquely use the data or violate privacy — a criticism leveled at OpenAI and others in a typical centralized GPT setting.

In fact, if you’re maximizing the strengths of both technologies, NFTs and gen AI, then:

• The embedded information on which an LLM conversation is based should be immutable, individualized, and decentralized.
• The conversation with such embedded information must preserve privacy. It does not need to reference any public GPT services.
• NFT creators must be committed to decentralized AI on Web3. Future iterations should and will allow a user to run their LLM model locally on the user’s own computer.

The protection of privacy possible with NFTs is a differentiator compared with using generative AI in a web application. A criticism of ChatGPT, BARD, and similar systems is that input from a user becomes data consumed by the generative AI, including proprietary input, and that can ultimately become available to other users. A generative tool like Microsoft’s Copilot stores input data, while assurances are made that it isn’t used for training large language models.

Still, the concern is that the power of massive data and its control becomes centralized even more by the hyperscaler giants of computing and the few smaller companies they back.

Blockchain’s fundamental decentralization and distribution means that generative AI NFTs can offer an alternative to that system of big data control.

Additionally, AI, as with mobile now, can leverage edge computing, not centralized cloud services.

Long-term Uses of Programmable NFTs in Life and Business

The viability of a technology depends on its ability to solve real problems and to create new possibilities and experiences beneficial to humanity and the world. Right now, we’re seeing a tremendous blooming of new uses for generative AI. When sharing information or when interaction needs to be secure, private, and impervious to short- or long-term systems failure or third-party dependency, the integration of NFTs and AI will prove especially handy.

In the personal realm, consider how you might preserve and interact with family history and important private documentation shared among family members over the long term, without pieces of that being scattered or permanently lost. Some families have members who live across the world and with whom they want to share information. Most people have family members dear to them who have passed away or will do so. Imagine an interactive record of your grandparents’ lives — one that you can have a conversation with. Imagine documenting family events from yesterday, five years ago, or fifty years ago that are less likely to be lost to time in a decentralized system. With programmable, AI-capable NFTs, you and your specified family members can own the assets with the data, the knowledge, the documents, and the interactive capabilities, as long as you have an internet connection. Digital existence and private access are distributed and not dependent on permission granted by a central authority or third party.

The same principles hold true for business uses.

Consider proprietary documentation, blueprints, and even trade secrets that need to be shared among specific engineers across a global workforce or from an older generation to a newer one within a company.

With an NFT, you could securely encrypt documentation, share it with an intended party, and, with generative AI capabilities, enable them to ask questions of it — even, in a sense, converse with engineers retired or long gone. Questions and answers about the documentation can instantly be translated into other languages.

Because NFTs are immutable and recorded permanently in a distributed public registry, impervious to deletion or third-party tampering, their potential for authenticating products and verifying lineage in the supply chain has often been noted.

Adding a layer of interactive inquiry with embedded generative AI makes this business use case even more exciting.

A programmable NFT or immutable “miniverse” with generative AI functionality is a new frontier in interactive, customized applications for personal and business use that truly puts ownership in individual hands. It’s time to lift NFTs out of the abyss of financial speculation and esoteric understanding and put the spotlight on their technical capabilities — which can make them useful for everyone.

[To share your insights with us, please write to sghosh@martechseries.com]

The post Generative AI Inside Programmable NFTs Is New — and Can Accelerate the Sector Beyond Hype appeared first on AiThority.

The rapid development of artificial intelligence (AI) is already starting to change the world. Advances in AI have made it possible to completely transform the user experience, and the demand is only growing. With the rising popularity of virtual reality (VR) headsets, more users are being introduced to this revolutionary technology at an earlier age. Research shows there are around 171 million people currently using VR worldwide, and out of those users, the vast majority are teenagers or younger. 

Over the past year, technology companies such as Meta began lowering the age restrictions for its VR apps to reach younger audiences, and while there are some restrictions in place now to ensure the safe use of these devices, this technology still poses a major threat to these audiences. The use of Virtual Reality technology can be beneficial for children if used responsibly, however, more action needs to be taken to better protect these audiences from the dangers of this disruptive technology. 

Online Safety in the Digital Age 

VR is not new.

The idea of using VR has been studied since the 1990s. Fast forward to today, healthcare companies, schools, and households are all harnessing AI-powered technology, and younger audiences are among some of the most frequent users. 

All technology generally has both positive and negative benefits to society, and VR headsets are no different. For example, new modes of learning delivery certainly should result in more effective education and children who enjoy the process more than traditional schooling.

Rather than just reading about a subject, a child can enjoy a fully immersive and interactive experience that can be much more enjoyable and effective than traditional methods.

On the other hand, many raise concerns about the safety and privacy of these devices. Many VR apps have already taken certain precautions to prohibit the unsafe use of these devices by children. Some of these restrictions involve requiring preteen’s parental approval to set up an account or young users only seeing apps and content rated for the pre-teenager age group. However, as previously mentioned, these limitations – while a good starting point – are not going to solve all the safety concerns that parents and guardians have with children using these apps. 

Identifying Friend from Foe

The age changes being made to these devices make children fall victim to nefarious individuals. VR represents a world that requires a nuanced understanding of potential threats because the cues that exist in the physical world can be more easily masked in VR. More specifically, the time spent in these connected worlds is a largely invisible experience, which causes serious issues when identifying friends from foes.

Pre-teenagers developmentally are less equipped to detect a threat to their physical or emotional well-being which requires this more nuanced understanding. Similarly, pre-teens are simply less intellectually and emotionally developed than older children. This presents an even larger risk to those in that younger age group.

Strangers in cyberspace can more easily impersonate “friendly” actors in VR, and that, combined with the lack of sophistication required for pre-teens to detect this, means a much bigger threat to all children – especially those who are younger. Additionally, pre-teens can be exposed to inappropriate and violent content without teachers or guardians being fully aware. There are also privacy concerns associated with VR devices. Several apps can collect data on users, such as eye movement and facial recognition, which many parents or guardians may not be comfortable with. For all of these reasons, there needs to be a better way to protect children when they are actively using these devices. 

A Better Path Forward to Securing the Metaverse 

The answer to this growing problem will undoubtedly lie in the involvement of parental figures.

Very strong controls around identity and content that children interact with must be implemented to protect them. More specifically, to protect children, all persons in the “spaces” that they interact in must have strongly authenticated and verified identities that can assert their relationship to the child, as well as assert permitted attributes that parents must approve before being allowed to interact with children.

For example, the real identity of the person and relationship to the child must be approved. 

Furthermore, the VR equipment itself must have controls to ensure that the person presently wearing it is an authentic and verified individual to whom the account belongs to prevent impersonation. Concerning content, strong controls around the age-appropriateness and classification of it must be implemented. This can be aided by AI to automatically detect and classify malicious content. All of these restrictions combined can better safeguard both children and pre-teens from the dangers of these devices. 

AI poses immense challenges for user security, most of which we are only beginning to understand.

Looking ahead, running age-appropriate and safe virtual experiences will become one of the most important challenges facing the world. As the popularity of VR devices continues to grow, particularly among younger audiences, both companies and parental figures will need to consider implementing strong controls. Once the security and identity threat is under control, only then can we begin to truly protect the health and safety of younger audiences in the metaverse. 

[To share your insights with us, please write to sghosh@martechseries.com]

The post To Help or To Harm: the Potential for Virtual Reality to Shape Future Generations appeared first on AiThority.

The impact of AI is unmistakable in every swipe on our smartphones and every click on our laptops, subtly shaping our digital interactions. It recommends articles, directs investments and refines our travel routes. This extends beyond mere personal benefits — AI is poised to significantly drive the global economy forward, with GenAI potentially boosting it by an impressive $13 trillion. This compelling economic benefit is a clear call to businesses: evolve with AI or risk falling behind.

Already, more than half of global companies are heeding this call, ready to amplify their investments to harness AI’s full economic potential.

Obstacles Mark the Path to AI-First Status

However, being an AI-first company takes a lot more than just updating software or getting the latest solutions; it takes a complete transformation of how businesses function, where we put the right focus, how we use technology, and the scale at which we innovate and operate.

Enterprises eager to integrate AI into their operations might first meet several critical speed bumps.

Endless Disruption:

AI is constantly evolving, accelerating transformations across sectors. Take banking, for instance. Its future is veering towards being a deeply personalized, intelligent, and omnichannel experience.

Imagine this: Sam, a working professional, seeks health insurance through her bank. Instead of the usual rate assessment, she receives a 2% discount on her premium, a perk dynamically calculated from her gym attendance and sleep quality. All this is streamlined by GenAI, slashing operational costs while delivering a hyper-personalized omnichannel experience.

Banks that harness AI and rapidly constantly evolve with it will be on track for exceptional growth.

On the other hand, those unable to keep pace stand to lose out significantly. The message is clear: AI is consistently transforming industries. Matching AI’s pace ensures robust growth and augmented shareholder value. Failing to do so, however, risks stagnation. The performance gap AI widens is undeniable.

SuboptimalTech Investments: 

When it comes to tech investments, it’s easy to get caught up in the latest and supposedly greatest, but that’s leading many companies down a costly path. Take this bank in North America as an example: they built a web of over 1,000 systems that are costing them $2 billion in ‘technical debt’ – the price we pay later for quick fixes made in a rush. This technical debt represents not just a financial strain but a significant obstacle to achieving a complete digital transformation.

A study indicates HR professionals navigate between three to six applications to complete a single task, a clear symptom of tool redundancy. Not only is the quantity of tools overwhelming, but the quality also leaves much to be desired, with reports of user-unfriendly interfaces and inconsistent performance.

The underlying issue is a misalignment of technology and business strategies. When we rush into new tech investments without a game plan that’s aligned with our larger business goals, we end up with a mishmash of old and new systems. It’s like trying to fit pieces from different puzzles together—impossible and expensive.

To avoid this, we need to think of digital transformation not as buying the latest tech but as a strategic rethinking that involves the whole company. It’s about choosing tools that integrate smoothly, keep our data in one piece, and make sure we are agile and can adapt as needed. That’s the kind of strategic thinking that will put us ahead and keep us there.

Pilots that don’t scale:

Pilot projects are popping up in boardrooms, each promising to be the next big thing in business transformation. But numbers show that despite the initial enthusiasm, a significant 70% fail to scale across the enterprise. Financial restrictions, operational complexities, and resistance to change clip the wings of widespread digital adoption.

These challenges mean that while certain areas of a business might enjoy the benefits of digital innovation, the impact doesn’t extend across the entire operation to user experiences.

This isn’t just about missing out on a few opportunities for improvement. It’s about the bigger picture: staying competitive. As digital initiatives fail in scaling up, businesses leave gaps for more tech-savvy disruptors to leap ahead. In short, we’re witnessing the paradox of progress: abundant technological advancements, yet a struggle to harness them for transformative change.

The great wall of Silos:

Take Sam’s case, where her dedication to fitness nets her a customized discount on health insurance, showcasing the power of AI to forge partnerships across banking, insurance, and health tech. This is the benchmark for an AI-first company: hyper-personalization at its finest.

However, what’s striking is that while this kind of inter-industry connectedness is the goal, many companies are struggling to achieve this seamless integration within their own walls. Silos are stifling the flow of information between people, processes, data, and tech, costing up to 30% of potential revenues and hindering the kind of integration essential for AI to thrive.

If Sam’s health data doesn’t speak to her financial profile, the opportunity for customized service vanishes. In this disconnect, the aspiration to be AI-first becomes unattainable. Breaking down these silos is an operational adjustment and a strategic imperative to unlock AI’s transformative promise.

These obstacles teach us an important lesson. Companies must ensure that their technology investments are not just new but right — right for their people, processes, and long-term strategic vision. Only then can they unlock the full potential of digital transformation and avoid the pitfalls that have ensnared many in a costly cycle of technical debt, unscalable pilots, and an overall disconnect in the enterprise.

Breaking Ground for a Customer-Focused, Automated, Data-Driven Future

Imagine a business landscape that not only adapts to market fluctuations but also forecasts them with pinpoint accuracy. This is the hallmark of AI-first enterprises.

Consider a leading supply chain organization that seamlessly predicts disruptions, adjusts its routes, and changes its course in real-time with the help of connected data systems, analytics, and AI.

How is this foresight achieved?

Their strategy is anchored in a customer-first or business-first philosophy. For them, transformation transcends digital—it’s about refining the entire user journey, enhancing it with technology, not being led by it. They place a premium on creating value through ecosystem partnerships and prioritizing human connections, tapping into contextual intelligence to guide their transformation efforts.

These organizations are steering towards a “hands off the wheel” future, where human ingenuity is the architect and sophisticated systems are the operatives.

Rather than focusing on cost reduction alone, they harness technology to amplify human capabilities. They aim for a high level of straight-through processing across essential customer journeys and business processes. By taking this approach, they don’t merely retrofit new technology into old systems. They design their processes with digital DNA from the outset.

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This strategic overhaul extends across all business divisions. In our supply chain scenario, it’s the convergence of logistics, inventory management, and customer service into a unified digital framework. Every department enriches the AI system, making decision-making processes not just quicker but also more intelligent and crafting a customer experience that is proactively engaging.

Does this mean an overhaul at the expense of existing investments?

Not at all. These businesses adopt a measured approach to reshaping their operating model. They strategically scale AI throughout their organization, taking a platform approach that connects all business operations, making digital engagement intrinsic to their identity.

[To share your insights with us, please write to sghosh@martechseries.com]

The post AI and the Future of Business: Overcoming Obstacles to Unlock a Trillion-Dollar Revolution appeared first on AiThority.

Today, everything is connected – from phones to watches to cars – modernizing industries, and cellular connectivity changing the way we live, work, and learn. But, at the same time carriers are building out the infrastructure to support this enhanced connectivity, they are setting aggressive decarbonization goals, including public Net Zero ambitions.

What that means for communications service providers (CSPs) is that they can no longer view performance as the sole criterion for success. The telecommunications network of today must be powerful and nimble – and it needs to be sustainable, too. And, AI is the key to creating and managing energy efficiency, reducing carbon footprint while continuing to deliver the high-quality service customers demand.

RAN Represents the Bulk of Energy Usage – And an Opportunity for Savings

The telecom industry is in a unique position when it comes to decarbonization. Advanced connectivity is key to enabling carbon reduction in other industries, and there is also a tremendous opportunity for the telecom industry to lead by example by cutting its own emissions. 

The Radio Access Network (RAN) equipment accounts for 80% of the energy use for an operator, based on a benchmarking study by the GSMA. Yet the GSMA also reports 62 operators, representing 61% of the industry (by revenue), have committed to a science-based carbon reduction target, pledging to reduce direct and indirect emissions by 2030.

As networks become increasingly complex, managing energy usage can’t be done manually. Software that monitors for periods of low usage to shut down unnecessary equipment is a good first step, but purely reactive solutions won’t be enough to manage traffic demands while cutting emissions.

AI is the missing ingredient that will help CSPs gain insights to inform ongoing energy savings, while also driving real-time efficiencies through operational orchestration.

AI is what will take network energy management from reactive to predictive, making decisions based on the network’s needs.

For example, AI functions can decide, based on data, what resources will be needed in the coming hours and days, and if all the capacity in the frequency bands within the RAN will be needed. They can then turn off or on different frequency bands or other resources according to predicted demand.

Because, AI programs dynamically learn, adapt, and act accordingly, these tools enable operators to control cells dynamically and in turn, serve dynamic traffic patterns instead of just peak traffic.

Taking things to the next level, CSPs will be able to use AI and machine learning predictions to build digital twins of the RAN environment, allowing them to test and develop energy-saving features without any risk to their actual, live network. 

AI Can Identify and Enable Savings Beyond Network Operations

And it’s not just the day-to-day network management where AI tools can help reduce energy usage. They can also help operators diagnose and resolve issues remotely, getting things right the first time to reduce unplanned downtime – as well as achieving carbon reduction through fewer maintenance truck rolls. And, with AI insights, CSPs can identify where new cell sites or other resources should be deployed, creating efficiencies with deployments and equipment buildouts.  

Another area where AI can enable energy savings is in the passive equipment at individual cellular sites, things like climate control or air conditioning.

AI solutions can help companies manage the energy infrastructure on-site more intelligently. They can also help in areas where demand response programs are enabled, or when government regulations or tariffs are in place to control peak demand.

For example, AI-powered applications can switch to battery power during times when tariffs are higher (peak load shifting), or when the grid power usage reaches a certain power grid alternating current (AC) limit.

There is a real urgency behind the need for CSPs to make operations more energy efficient, as the telecom industry works to meet self-declared sustainability targets as well as government mandates. Yet, there can be an understandable hesitancy to enact some energy-saving solutions, for fear of disrupting networks or reducing the quality of service. Luckily, AI can be a solution for both these issues – giving CSPs the tools they need to manage complex network functions in the most efficient manner possible, while also collecting and analyzing data to test and refine new applications in a virtual environment, such as a digital twin. Cellular networks need to get smarter to meet sustainability expectations without affecting customer experience – and AI is the key to making it work.

[To share your insights with us, please write to sghosh@martechseries.com]

The post AI-Enabled Solutions Are the Key to Help Cellular Operators Reach Carbon Reduction Targets appeared first on AiThority.

Virtual and augmented reality have long been touted as exciting new technologies that could upend platforms firstly in gaming, like PC and console. But mass market adoption has so far been a challenge.

Meta has gradually been building a market of millions of users – the Meta Quest range has sold approximately 20 million headsets to date – but it’s been a slow burn, with reported low levels of engagement and retention from owners becoming a critical issue.

Meanwhile, augmented reality has yet to take off for gaming. Niantic’s multi-billion dollar hit Pokémon GO is the clear flagship title for the technology, but to date, no other title has been able to match that success, or even come close. As for AR headsets, any attempts to launch consumer hardware have failed.

One of the main selling points for AR and VR is immersion, whether in an augmented real-world environment or an entirely virtual space. Along with graphical fidelity, artificial intelligence (AI) is key to creating these experiences.

AI is being used to create different worlds and characters that respond and adapt to the user’s presence, thereby increasing immersion. Cracking this element is one of the keys to building VR’s killer app.

A new generation of immersion… Lead by gaming

Non-playable characters (NPCs) and enemies are powered by AI, and how they interact with players can shape the entire gaming experience and make or break immersion.

Slowly, we are seeing the introduction of NPCs across other industries. In simulation situations, often for training purposes,  virtual agents are used who have characteristics, abilities, and actions that closely mirror those of NPCs in games. The characters are assigned certain behaviors, which they then exhibit within the simulation. In the recruitment process or training simulation they can bring a once dry and formulaic experience to life. As the adoption of AI in business grows, NPCs will be used in areas of the business that require an injection of creativity to drive engagement.

How Can Businesses Benefit from the Edge AI Boom?

While NPCs are not to be confused with bots, AI is also set to change the evolution and experiences possible through the use of chatbots, which are often used in customer help desk situations. Meta recently launched Messenger for its AI studio, which allows companies to create AIs that reflect their brand’s values and improve customer service experiences. While this product is currently in Alpha testing, celebrities, such as Snoop Dogg, Kendall Jenner, and many more, are said to be available as AI characters to test. This will make AI chatbots more personable and interactive and strengthen customer brand engagement and experience.

AI can also be used for creating more dynamic experiences, such as cueing dynamic music based on actions, or for raising and lowering difficulty levels in simulation training. It can even take things a step further, adapting the simulation or training, based on progression, play styles, and preferences.

Generative AI is a cutting-edge technology, which most recently became widely popular through large language models like ChatGPT and image generators e.g Midjourney – but the capability of the technology is so much more. The technology can be harnessed to create unique responses to prompts and actions to build unique experiences and is being used across a wide range of industries. However, ethical issues around images and data used without consent (which is a possibility for some models) are a grey area that has caused contention across many industries and is something that governments globally are looking to address with regulation for wider global implementation and innovation to be driven through the technology.

AI agents are already being used across several sectors – from architecture (Chinese architectural AI XCool recently launched LookX) to automotive, with examples in leading brands like Tesla where AI is used for innovative safety features. AI can dynamically interact with users based on predefined actions and responses, choosing the most appropriate reaction for the situation. This can be utilized for a variety of experiences, such as guided tours or even simply customer support. In VR, where immersion is a key selling point, the opportunity to have more individualized and personable interactions with NPCs and other AI agents could help improve this further.

There are also other possibilities once generative AI tools make it into developer workflows. Whether based on external or internal content libraries, the technology can power the creation of new art assets, visual effects, and more, assisting artists with their work in crafting immersive worlds.

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Building killer apps

For AR, AI is used to help seamlessly add augmented content to our surroundings in the virtual space. For many, the introduction of AR was led by the launch of Pokémon Go in 2016, played on mobile and allowing users to interact with objects in the real world around them, to monsters scaling and destroying buildings or just artwork on a wall, AI is the critical element in AR simulation adapting to the physical world.

Developments around AI come at an interesting time for the VR and AR markets. Meta has been leading the charge in the VR market, with a third iteration of its Quest headset due to be launched imminently. Apple is stepping into the sector with its mixed reality – or as it calls it, ‘spatial computing’ – headset Apple Vision Pro, which itself could be a game-changing moment.

Niantic and a host of other tech giants and start-ups are also hard at work developing new, groundbreaking AR technology that can power future experiences on headsets and smartphones. We are also starting to see a breakthrough in the consumer fashion/tech space with the recent announcement from Ray-Ban on its smart glasses with two 12-megapixel cameras by each eye and an LED light that flips to alert others that you’re recording. The next iteration of this product will include AR technology.  With the ability to livestream through the glasses to friends and family and over 150 design options, the glasses will retail at around $300, a similar price to much of the brands’ standard range.  We will then finally start to see the cost of VR/AR products to the consumer market reducing over time to become more accessible.

While the AR and VR market ultimately became one that was too expensive or complicated, we are certainly seeing a resurgence, with AI powering the next generation of killer apps that AR and VR sorely need to improve engagement and hit the mass market with several exciting applications outside of the gaming world.

[To share your insights with us, please write to sghosh@martechseries.com]

The post AI Set To Drive Virtual And Augmented Reality Market Growth appeared first on AiThority.

In the era of accelerated digital innovation and fierce competition, businesses that possess a robust brand identity outpace rivals. With that in mind, many turn to creative agencies to create branding that will make its mark on consumers’ minds.

And when it comes to digital innovation, the creative industry itself is witnessing a seismic shift with the adoption of Generative Artificial Intelligence (Gen AI). In recent years it has become a game-changer in brand development, offering efficiency and a more substantial visual impact. Gen AI pushes current creative boundaries, from developing intricate digital artwork to helping visualize inventive branding initiatives. The integration of conventional creative strategies and AI signifies a new age of branding, amplifying the quality of work that agencies deliver to their client base.

For example, AI-powered tools enable creatives to produce designs and ideas in a substantially shorter time, allowing for a sharper focus on refining and perfecting their vision for faster outcomes. Improved quality is achieved through AI-assisted visual communications, offering polished, well-aligned, consistent imagery, and empowering brands to craft captivating branding components that resonate with their intended audience.

This seamless collaboration between man and machine is fostered through comprehensive and reactive prompts from the creative team, facilitating an engaging exchange that accomplishes the desired results more efficiently.

Case study: combining intelligent branding and a customer-centric approach

Using AI for Vibrint – a trailblazer in AI within the intelligence sector – was apt and strongly resonated with their company values. Using AI platforms such as Dall-E3 (via ChatGPT), Midjourney, and Runway ML, we were able to amplify our creative work to create fluid and dynamic animations that successfully captured the ever-changing aspect of intelligence analysis and decision-making.

This is essential in the competitive tech industry in which B2B firms, especially when competing against more prominent B2C brands, must present an appealing, vibrant, and dynamic image. This helps them stay ahead of the game and attract the best talent.

Creating a tangible logo and adopting a colorful, dynamic identity helped us separate our identity from other B2B brands that often resort to abstract symbols and uninspired visuals. Our brand symbol, inspired by a stylized falcon, symbolizes vigilance and agility.

Positioned above the brand name, it represents readiness and action.

Moreover, a robust customer-centric strategy is crucial for demonstrating a brand’s dedication to its users. For us, this approach emphasizes the central theme of ‘accountable intelligence’ and the promise to help customers ‘make the right call.’ Vibrint’s ability to analyze vast data volumes with expertise sets it apart, putting the ‘accountable’ aspect at the heart of its operations. By focusing on benefits and outcomes instead of activities and processes, customers are better connected to company values and can better understand why they should engage with us.

Read More: Top 20 Uses of Artificial Intelligence In Cloud Computing For 2024

Changing the creative game with AI

Gen AI tools with natural language abilities help creative professionals notably boost productivity and conserve valuable time and effort in generating fresh concepts or content. Our collaboration with Vibrint is a strong example of how AI, combined with human creativity, can augment and revolutionize creative processes.

Dall-E3, Midjourney, and Runway ML proved to be invaluable assets for creating static and moving images. Using comprehensive prompts helped our team capture every detail, from the subject matter, style, and aesthetic to composition, theme, mood, and context, and even specific elements such as lighting, dimensions, and perspective. Runway ML specifically helped with image enlargement, low-resolution image improvement, and converting basic written prompts or static image inputs into animated video content.

Creating a truly authentic brand means deep diving into its core beliefs and values, moving beyond plain authenticity to forge trust amongst the target audience. While this process can generally be time-consuming, AI presents a valuable means of crafting a solid and efficient brand strategy. By automating time-consuming operations, designers can focus on delivering precisely what they are most passionate about – creative brand stories.

AI ML Story: A Computer Vision System Accurately Computes Real-Time Vehicle Velocities

[To share your insights with us, please write to sghosh@martechseries.com]

The post Generative AI: A Revolutionary Force In The Creative Industry appeared first on AiThority.

The shifting sands of consumer behavior demand the undivided attention of brands and marketers as we head into 2024. My top predictions for the industry in the new year largely underscore the power of technology as it relates to consumer behavior. The following predictions, grounded in industry trends, can serve to shed light on the transformative forces shaping the marketing landscape.

AI Takes Center Stage

In the coming year, Artificial Intelligence (AI) is set to become the backbone of marketing operations, ushering in an era of real-time interactions with consumers. The projected generative AI market size of $1.3 trillion by 2032, according to Bloomberg, underscores its immense potential in reshaping the marketing landscape. Brands that harness the power of AI stand to benefit from enhanced customer engagement and personalized experiences.

CTV Emerges as a Dominant Force

A shift in advertising expenditure is anticipated in 2024, with Connected TV (CTV) poised to overtake traditional TV. The expected 22.4% growth in CTV ad spending, reaching $30.1 billion, (Insider Intelligence | eMarketer) signals a redirection of advertising dollars toward the burgeoning CTV space. Marketers should pay close attention to this trend, recognizing the changing dynamics of consumer attention and adjusting their strategies accordingly.

Recommended: AI in Gaming Predictions for 2024: Featuring Industry Experts from AppLovin, Adjust and Wurl

Social Media’s Retail Renaissance Continues

Social media’s role in the retail landscape is set to evolve further, with e-commerce projected to account for 23% of global retail sales by 2027, according to Statista. Top retailers are expected to deepen their collaboration with leading platforms and creators to craft seamless, personalized experiences for consumers. This renaissance on social media underscores the importance of a strategic and integrated approach to online retail.

With the new year presenting both challenges and opportunities in the rapidly evolving digital landscape, marketing professionals need to focus on key investment priorities that will shape their strategies and ultimately ensure campaign success. These top investment priorities, according to marketing professionals, can guide brands in navigating the dynamic terrain of consumer behaviors.

First-Party Data Dominance

With 75% of marketers heavily relying on third-party cookies in 2023 according to MarTech, the impending deprecation of third-party cookies by Google has elevated the importance of first-party data. Access to first-party data will be a priority, providing marketers with valuable insights into consumer behavior.

Emphasis on Measurement

Over one-third of marketers are expected to prioritize investments in measurement tools. Brands that identify tools that support strategies like always-on attribution and real-time optimization will be able to maximize reach and increase their likelihood of campaign success in an increasingly data-driven environment.

Omnichannel Solutions for Enhanced Customer Experience

The critical importance of maintaining a presence across various channels is highlighted by a projected 10.1% growth in click-and-collect sales through 2024, according to Insider Intelligence | eMarketer. Omnichannel solutions will significantly impact both the customer experience and Return on Advertising Spend (ROAS).

These 2024 predictions and priorities can serve as a roadmap for marketers, providing them with a foundation for strategic planning and long-term campaign success in the year to come.

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[To share your insights with us, please write to sghosh@martechseries.com]

The post Navigating the Marketing Landscape in 2024 appeared first on AiThority.

With barely a few days left in 2023, I am offering my views on the adoption and impact of generative AI in legal industry in 2024, in the context of the knowledge management function.

Generative AI will deliver value to those who know how to use it, the tool doesn’t understand your legal function

Whilst generative AI is becoming synonymous with AI, the reality is that the latter isn’t a single technology but a cluster of different technologies. Generative AI applications, on the other hand, specifically use natural language processing on data provided to deliver results and outcomes in response to precise requests. The generative AI application doesn’t understand the legal concepts underlying the questions that it is asked or the words/output that it produces. So, legal expertise within the context of matter strategy and complexity is needed to sanity-check the outputs the generative AI tool throws up.

Consequently, generative AI will deliver value and convenience only to those who know how to use it, and even help fine-tune thought processes to achieve the desired outcome. For instance, the same question can be asked in multiple ways, and generative AI will respond in different ways. Likewise, lawyers can receive different outputs to the same questions, depending on the documents that they are ‘authorized’ to access in the firm. The tool is processing natural language, after all!

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Microsoft will enable generative AI adoption, but compliance and confidentiality will be showstoppers

In the near term, the first ‘showstopper’ for meaningful use of generative AI will be compliance.  A lawyer using generative AI (be that co-pilot or its adaptive version) to create a report for sharing with clients or third parties needs to ensure that the document complies with all the applicable data protection and data privacy regulations. In addition to enabling generative AI adoption through the provision of a co-pilot, Microsoft will play a role in ensuring that data security and residency aren’t compromised.

Microsoft’s security policy for Azure ensures that data residing in individual organizations’ cloud tenants is not shared externally.

So, it will be incumbent on law firms to only use data that is within their own cloud tenant, much like it was in previous years where data resided within firms’ networks.

The second showstopper is going to be data confidentiality. At an individual user level, people will only have access to documents they are authorized to view, based on the principles of need-to-know security. This means that individuals using the firm’s generative AI tool will only surface results based on the data they are authorized to access. In some cases, the tool may produce output based on a completely different set of documents to another individual. This will possibly impact the value of generative AI in terms of contextual accuracy, currentness, and suitability.

Without a trusted knowledge source, your generative AI tool will deliver limited value

Document management systems will be the logical place for law firms to embark on their AI journey, which, BTW for many firms, have already started.

For instance, AI tools are embedded in document management systems to assist with document classification, automatic filing of emails and documents based on user behaviors, and even interrogating the application for information by asking specific questions. Note here that for this kind of broad AI adoption, there is no need to “train” the tool for it to deliver the desired results.

To adopt generative AI and ensure that the tool is “trained” on the most accurate, authorized, and current data, firms will need to create a central, curated repository of trusted data – i.e., the knowledge management system. This will ensure the best and most useful output. To illustrate, a lawyer would be able to instruct the firm’s generative AI tool to create an 800 words abstract based on a 100-page M&A contract, that refers to participants from the US and Germany and pertains to the New York jurisdiction – and be assured that the right data has been used to create the output.

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It isn’t the ‘rise of the machine’ in legal

Finally, despite the hype surrounding generative AI, we aren’t heading towards a ‘rise of the machine’ scenario in legal. There are many issues to iron out – from hallucinations and security to ethics, data management, and regulation – which means that human intellect is very much indispensable. It’s true that AI technology more broadly has a lot to offer in the form of support, convenience, and efficiency – and that over time generative AI will become more accurate. But natural language processing is exactly that – i.e., language processing. Generative AI engines know what letters or words to put one after the other, but it does not understand the words or concepts and needs detailed instruction and high-quality data to deliver value.

[To share your insights with us, please write to sghosh@martechseries.com]

The post A Trusted ‘Knowledge’ Repository is Key to Generative AI Adoption in 2024 appeared first on AiThority.

SnapLogic Chief Scientist Greg Benson reveals his predictions on Generative AI, and the challenges and opportunities it presents in 2024.

Prediction 1 – GenAI won’t take your job, but it might change it.

In 2024, Generative AI won’t lead to mass job displacements and redundancies, as many early sensationalist reports might have suggested. GenAI won’t completely replace experts in any field, as although models have access to an insurmountable amount of information, users still have to articulate concepts well enough to get the right answers – thus, expertise and human input and, more importantly, human review always will be necessary.

Collaboration with GenAI is a trend we can expect to continue in 2024, as businesses look to capitalize even further on its benefits, reaping the rewards of increased productivity and quality of content creation. This means adopting even more GenAI tools and encouraging even more use of them; the goal being not to replace workers, but instead assist what they do.

Prediction 2 – Now that we’ve invented GenAI, the next step is understanding it.

Next year, we can expect to see businesses attempt to improve the consistency of output from Generative AI. Currently, there is no set rule book for achieving great results with GenAI; there are tips and tricks you can deploy for better or faster results, of course, but overall the process is largely trial and error.

Interacting with GenAI in its current iteration is like a science experiment – you come up with a hypothesis and continue to test different manners of prompts until it produces the result you’re looking for. In the future, the focus of experimentation will be on figuring out how we evaluate the responses it gives us and using that data to inform prompts further.

Companies that want to apply GenAI to their products will need to think about how they carry forward and evolve prompts that can improve results directly. Qualitative and quantitative improvements can only be brought about by reevaluating their approach to AI application and development.

Prediction 3 –Expect an onslaught of GenAI tools and GenAI startups.

In 2024, we’re going to see another year of the AI market expanding, with more variety as GenAI startups try to find their niche among the masses.

Rather than consolidation, more GenAI solutions will continue to pop up in different industries. Of course, there will be a lot of attempts that don’t get traction, or just don’t work, but this won’t deter the wave of opportunistic entrepreneurs and businesses who look to capitalize on the GenAI wave.

There’s already the start of a huge race on the hardware side too: companies such as Google and AWS are building their own AI hardware in addition to NVIDIA, which is worth watching. If successful, these advances in hardware could lead to another explosion in how large language models are trained, as currently, it takes a lot of human input, money, and effort to build from the ground up.

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Prediction 4 – GenAI regulation is essential to adoption.

Regulating GenAI will be a huge focus for governing bodies and business leaders in 2024. Earlier this year, calls were heard for a pause in AI development from numerous visionaries, but this isn’t realistic as the fundamental technology is increasingly available through open-source models available on Hugging Face. Rather than focusing on halting development, creating clear regulations, guidelines and best-use practices will be necessary to ensure partnership with AI will move forward safely and securely.

Like any other technology, defining the boundaries that keep safety in mind will allow for leveraging the benefits without sacrificing progress. We can liken this to all manners of tools and equipment that need to be regulated; for example, we don’t stop ourselves from building cars that go fast, but we do put speed limits in place to ensure safety. Internationally, governments will draw their attention first to the areas of regulation that present the greatest impact on citizens, including frontier AI.

From an industry perspective, the GenAI applications and most helpful cases will emerge as front runners for wider business use cases. Understanding the risks, challenges, and security issues potentially imposed by these tools will be vital for businesses to understand exactly when and how these tools need to be regulated internally.

Likewise, companies hoping to leverage GenAI will have to communicate to customers exactly how it’s used and how it complies with current and future regulation requirements.

Prediction 5 – GenAI and Legacy technology: Why the key to modernization may reside in GenAI tools.

After a year of GenAI practice, legacy businesses are starting to understand that GenAI interest is not just driven by ‘hype’, and instead could be truly transformative for their sector. Therefore, in 2024, we can expect even more traditional businesses to deploy the technology to help evolve legacy systems and modernize their technology stack.

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Typically, traditional companies are not amenable to change or agile enough to adopt the latest in new technology. Many companies are tied to legacy software due to a combination of outdated procurement processes, familiarity, or concerns about data loss or disruption, making modernization inaccessible. The key here is that GenAI can assist with migrating off old code bases and technology stacks to modern programming languages and platforms.

However, GenAI could bridge this gap by allowing companies previously locked into legacy systems to access a more modern workforce’s knowledge and work practices. GenAI also makes some modern tools far more user-friendly, and therefore more likely to be deployed across businesses.

Prediction 6 – AI and the question of originality

Next year we’ll see the average person become more adept at using AI, both in business and in their personal lives. Students will also interact with GenAI at a greater scale.

On the one hand, ChatGPT and others can be great personal tutors to help students understand concepts. On the other hand, ChatGPT can be used to generate solutions to problems. I tell my students that they can use GenAI to help them as they are learning, but they must turn in their own original work. The problem is that it is extremely tempting to have GenAI provide the answers, perhaps just partially. In addition, since the answers are coming from a computer program and not another student, it distances students from the notion they are cheating. So far, for my classes in computer systems, it has been fairly easy to determine if a student has turned in GenAI solutions because they don’t follow the conventions in code that I’ve taught in class and require in student solutions.

How GenAI is used in classrooms is very much a work in progress. At the moment there’s still no best practice model – even at my University, we have workshops about AI, but no succinct policy. Beyond the classroom, there is the larger question of intellectual property and how GenAI is trained on internet-accessible creations and works available in digital form. We will see this play out in all industries and the courts in 2024.

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Prediction 7 – Universities will begin to teach prompt engineering

In 2024, universities will teach prompt engineering as a minor field of study and through certificate programs. Prompt engineering for GenAI is a skill already augmenting domain experts, similar to how computing has augmented other domains. The successful use of large language models (LLMs) relies heavily on giving the models the right prompts. When looking to fill the role of a prompt engineer, the task becomes finding a domain expert who can formulate a question with examples in a specific domain, a skill critical for today’s IT

professionals to refine to successfully implement LLMs. Given this, universities will introduce new academic focus areas to address the growing demand for professionals with specific skills required to build the next generation of GenAI applications.

Currently, SnapLogic is the leader in generative integration. As a pioneer in AI-led integration, the SnapLogic Platform accelerates digital transformation across the enterprise and empowers everyone to integrate faster and easier.

Whether you are automating business processes, democratizing data, or delivering digital products and services, SnapLogic enables you to simplify your technology stack and take your enterprise further. Thousands of enterprises around the globe rely on SnapLogic to integrate, automate, and orchestrate the flow of data across their business.

[To share your insights with us, please write to sghosh@martechseries.com]

The post SnapLogic Chief Scientist Reveals GenAI Predictions for 2024 appeared first on AiThority.

The way businesses engage with customers is an ever-evolving practice, and depending on how it’s conducted, it can have a direct reflection on important measurables including revenue. Independent Software Vendors (ISVs) are continuously striving to enhance user experiences by leveraging today’s technologies.

Innovation, Agility, and Integration

ISVs are typically known for their ability to adapt and respond accordingly to specific needs. They can quickly respond to market demands and technological advancements, introducing new features and capabilities that can improve the overall customer experience. ISVs develop software that can seamlessly integrate with existing systems and platforms, and that integration capability is vital for businesses looking to enhance customer engagement without disrupting current workflows.

Many ISVs also specialize in Customer Relationship Management (CRM) solutions, helping businesses manage and optimize their interactions with customers. These systems centralize customer information, streamline communication, and enhance overall engagement.

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Transforming User Experience Through Collaborative Innovation

Here are just a few ways in which ISVs modernize user experiences:

• User-centric design: ISVs adopt user-centric design principles to create intuitive and user-friendly interfaces. They conduct thorough user research to understand the needs, preferences, and pain points of their target audience.

• AI and machine learning: Incorporating artificial intelligence (AI) and machine learning (ML) algorithms enables them to provide personalized experiences. This includes features such as recommendation engines, predictive analytics, and intelligent automation, enhancing the overall user experience.

• APIs and integrations: Their open APIs allow seamless integration with other software and services, meaning users can connect their preferred tools and enhance functionality to suit specific needs.

• Continuous feedback: ISVs use agile development methodologies, which involve continuous user feedback and updates. This allows them to quickly respond to changing user needs and preferences, ensuring that the software remains relevant and effective.

The Future Lies in Personalized and Secure Engagement

ISVs often focus on creating niche and specialized software solutions tailored to specific industries or business processes. Those specialized tools enhance customer engagement by addressing unique challenges and providing targeted functionalities. One such example is that their solutions can be customized to meet the specific requirements of individual customers. This level of personalization enhances the customer experience and fosters a sense of ownership among customers. When customers feel that a product has been tailored to their needs, they are more likely to remain loyal to that product.

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It’s also important to note their relationship with security and compliance, which are increasingly critical and ISVs focus on developing solutions that prioritize data security and adhere to industry regulations – which is essential to maintaining customer trust.

Integrated Payments: A Game-Changer in the Payment Ecosystem

ISVs play a crucial role in the payment ecosystem because businesses of all sizes are increasingly drawn to software platforms that offer more than basic functions. By integrating payments directly into their software platforms, ISVs offer a seamless user experience. Integrated payments allow ISVs to grow the size of their market with roll-out processing solutions around the world via cloud-connected processing partners. Integrated payments also offer excellent flexibility in designing a preferred checkout process and make it easier to introduce new payment methods such as Buy Now, and Pay Later (BNPL).

ISVs are a key player when it comes to the future of customer engagement and they may be invaluable when it comes down to retention. The specialized, innovative, and scalable software solutions can align with the evolving needs of a business and its customers. The future of engaging with customers means forming strong relationships that blend well with the technology available today, and innovations to come.

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[To share your insights with us, please write to sghosh@martechseries.com]

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