Category: What’s New in Technology

Lately, there has been a lot of talk about quantum computing, drawing interest from many, including business leaders. Quantum computing promises to solve previously unsolvable problems and revolutionize entire industries. As a result, excitement around its potential is rapidly growing. However, it is important to first ask where the hype ends and the real business value begins.

What is Quantum Computing?

Simply put, quantum computing is a new way of processing information. Unlike classical computers that use bits that are either 0 or 1, quantum computers use qubits (quantum bits). Qubits can exist in multiple states simultaneously as enabled by the principles of superposition and entanglement. This allows quantum computers to process vast amounts of information in parallel. Hence, quantum computers can theoretically tackle certain classes of problems that would take classical computers years to solve.

The Hype: Quantum’s Promised Revolution

Quantum computing is said to have the potential to perform tasks such as cracking encryption, revolutionizing drug discovery, optimizing global supply, and transforming artificial intelligence. Forecasts like one from Boston Consulting Group (BCG) project that quantum computing could unlock up to $850 billion in economic value by 2040. As a result, major industries are investing heavily and hoping to be among the first to benefit from a potential industrial revolution.

The Reality: Technical and Practical Challenges

The reality tells a different story. Today’s quantum hardware is still in its infancy, with most of these computers having fewer than 100 reliable qubits. They face issues such as noise and error rates that make large-scale practical applications elusive. Unlike classic chips that can be stacked for scaling needs, quantum systems can’t be easily scaled and need major advances in architecture and interconnects. Specialized expertise is also required to develop software for quantum machines. Besides, the algorithms that fully exploit the quantum advantage are still being researched. McKinsey estimates that while there may be many operational quantum computers by 2030, their ability to solve complex problems will take more time to mature.

This isn’t to say there is no hope as more improvement is made to quantum computing every day. Consider Google’s Willow, a 105-qubit processor introduced in December 2024. Willow addresses the error correction challenge and performs certain computations in under five minutes, which would take a supercomputer 10 septillion years.

Real-World Business Applications

Despite these challenges, quantum computing has demonstrated potential in real-world use cases. One example is Volkswagen who partnered with quantum computing firms to optimize traffic flow in Lisbon. This demonstrated how quantum algorithms can improve urban mobility. In finance, quantum-inspired algorithms are being tested for portfolio optimization and risk analysis by companies like JPMorgan Chase. Pharmaceutical companies are also testing molecular interactions with quantum simulation to potentially accelerate drug discovery. It’s worth noting that these applications are mainly hybrid solutions that use both quantum and classical computing. Even so, it signals there is potential in future breakthroughs.

Cloud-based quantum computing availed by platforms like IBM, Microsoft and Google have greatly contributed to this venture. These resources have made experimentation possible without the need for in-house quantum hardware. Therefore, businesses have a chance to innovate solutions to complex problems more affordably.

An example of a strategic framework that can help business leaders is the “quantum economic advantage” developed by MIT and Accenture. It requires two conditions: a quantum computer capable of handling the problem’s size (feasibility) and a quantum algorithm that outperforms a similarly priced classical solution (algorithmic advantage). Only when both conditions are met does quantum computing become economically beneficial.

How Businesses Should Get Ready for Quantum Computing

Preparing for quantum computing doesn’t require immediate transformation; however, it does call for strategic foresight. Here’s how businesses can begin laying the groundwork today.

• Create a Quantum Strategy: Identify potential long-term use cases where quantum could offer an edge, and develop a roadmap aligned with industry trends and business goals.
• Invest in Collaboration and Research: Partner with universities, quantum startups, and industry groups to stay updated and explore early-stage innovations.
• Start Quantum-Proofing Security: Begin evaluating quantum-resistant encryption methods to safeguard future data as quantum threats to cybersecurity emerge.
• Experiment Safely: Use cloud-based quantum platforms to run small pilots or simulations, gaining hands-on experience without major commitments.
• Build Internal Capability: Upskill current staff in foundational quantum concepts to ensure your team can engage with this evolving technology when the time is right.

Final Thoughts

Quantum computing is in its early stages, but its disruptive potential and rapid development give businesses a reason to start planning on its adoption, or risk falling behind. Integrating quantum has the potential to boost efficiency, cut costs, and enable innovative products and services. To stay competitive, businesses should start building a quantum-ready workforce through training, hiring, and academic partnerships.

Deepfakes are becoming more convincing than ever. Whether manipulated media or entirely generated by artificial intelligence (AI), deepfakes can now realistically alter faces and clone voices. They can even fabricate entire scenarios across video, audio, and text. Unfortunately, these developments now create significant challenges, and people can no longer trust what is presented online. Methods that have in the past been used to detect less-perfect deepfakes are becoming obsolete. There is now an urgent need to develop more effective detection solutions.

The Escalating Threat

Deepfakes are being actively used in malicious ways. It is being used to fuel misinformation, enable new forms of fraud, and erode the foundations of digital trust. An Identity Fraud Report 2024 by Sumsub noted a four times increase in the number of deepfakes detected worldwide from 2023 to 2024. A research study by iProov tested 2,000 UK and US consumers, revealing that only 0.1 percent of the participants accurately distinguished between real and fake content. These are only a few statistics on the severity of the deepfake problem.

Limitations of Current Detection

There are various tools and technologies available for detecting deepfakes, ranging from manual forensic analysis to automated AI-based solutions. These methods rely on identifying issues such as inconsistencies in blinking patterns, facial warping, extra limbs, or audio glitches. However, new AI models creating deepfakes have advanced to minimize these problems.

Therefore, relying on known flaws to detect deepfakes is not a sustainable strategy in an ever-evolving landscape.

Innovations in Detection Modalities and Speed

Innovation in deepfake detection requires an approach that will address the complexity and diverse nature of modern synthetic media. The new innovations must move beyond analyzing just one type of media.

• Multi-Modal Detection – The latest deepfakes are multi-modal and can manipulate video, audio, and even accompanying text simultaneously. Therefore, detection software must have the capability to analyze these elements together.
• Focus on Voice and Audio – This is especially crucial in detecting sophisticated voice deepfakes used in scams. New software is being built to analyze subtle vocal characteristics, background noise inconsistencies, and even speech patterns in combination with any available video to verify authenticity.
• Real-Time and Scalable Solutions – There is a need for advanced systems that can detect deepfakes quickly and efficiently in livestreams and large volumes of content. Detection system developers must develop algorithms and infrastructure capable of this speed and scale.

Advancements in AI for Deepfake Detection

AI is playing a major role in the development of next-generation detection software that is beyond simple artifact detection to more sophisticated analysis.

• Leveraging Foundation Models – Researchers are exploring large, pre-trained AI models that are behind many generative tools. Since these models are trained with vast amounts of data, they understand natural media. They can be fine-tuned and incorporated into detection software to help spot deviations that indicate synthetic origin.
• Proactive and Generative Approaches – Some innovations are proactive, where generative models are being used to understand how fakes are made. This will allow detectors built into software platforms to anticipate and identify novel manipulation techniques even before they become widespread.
• Towards more Robust and Explainable AI – Software development is also focusing on robustness against adversarial attacks. New training methods are being implemented to make detection software more resilient to deliberate attempts at evasion. There is also a push for Explainable AI (XAI) within detection software. This will help users understand why a piece of media was flagged.

Authentication and Verification Beyond Pure Detection

Advanced detection is bound to be challenged; therefore, next-generation solutions are incorporating methods for authentication and verification built into software systems.

• Blockchain and Media Provenance – Exploring how blockchain technology can be utilized to create immutable records of media origin and any subsequent changes.
• Human Element and Crowd-Sourcing – Integrating human expertise as a judgment of human expertise will help in complex cases. Crowd-sourcing expertise is also being explored as a way for platforms to scale human review.
• Detecting Deepfakes in New Frontiers – As digital interactions move into new spaces like virtual worlds and the metaverse, detection software for these platforms is also necessary. This will help identify manipulated avatars and synthetic content within the immersive environments.
• International Collaboration and Standards — fighting deepfakes is a global challenge, as synthetic media can easily spread worldwide. Therefore, collaboration among international researchers, governments, and technology companies is crucial. To accelerate the development and deployment of effective countermeasures, the involved parties can share data on new deepfake techniques and detection methods, as well as common technical standards.
• Public Awareness and Digital Literacy – educating the public on how deepfakes are created and what to look for empowers them not to be duped by fakes. Promoting digital literacy helps people evaluate online content more skeptically and understand the importance of verified sources.

Conclusion

The race between deepfake generation and detection will undoubtedly continue. The ongoing development and deployment of sophisticated detection software is an important step toward safeguarding the integrity of digital media and preserving trust in everyday digital interactions. To deal with the escalating deepfake threat, passive defense is insufficient. Therefore, it is recommended to prioritize adopting integrated, next-generation detection software and verification methods to safeguard operations and trust.

Competition in business today has become fierce. Each organization is constantly looking for innovative ways to form strong relationships with its customers. Loyalty programs have been used for a long time to build a devoted customer base. As technology advances, new technologies like Web3 are emerging, offering more opportunities to revolutionize loyalty programs, build vibrant communities, and deepen customer engagement.

Transforming loyalty programs through Web3

Loyalty programs help boost customer spending and drive long-term business success. Loyalty program members also generate more revenue than non-members. In the United States alone, the average consumer belonged to more than 15 programs in 2024. However, traditional loyalty programs have encountered problems that include customer disengagement and unclaimed rewards.

Web3-based loyalty programs address these problems by leveraging blockchain technology to create a more engaging, transparent, and valuable experience for customers. With the global Web3 market having a valuation of $4.62 billion by January 2025, there is enormous potential for businesses to innovate in this space. Web3 is the next iteration of the internet, which will help businesses create deeper customer connections through decentralized technologies like blockchain, non-fungible tokens (NFTs), and decentralized autonomous organizations (DAOs).

Why Web3 Loyalty Programs

1. Enhanced personalization and security
   Web3 loyalty programs provide enhanced customer engagement through hyper-personalization. Businesses can utilize blockchain technology to analyze customer preferences, behaviors, and interactions to customize rewards. This makes every customer feel valued. Using this approach, it becomes easy to focus on those customers who drive the majority of engagement and revenue. The decentralized nature of blockchain also ensures that data remains encrypted, secure, and only accessible with explicit consent.
2. True ownership of rewards
   In traditional programs, loyalty points exist only within a company’s database. However, Web3 platforms create unique tokens that a customer can own and control. When customers have this kind of authentic ownership, it changes how they perceive and engage with loyalty programs that allow greater flexibility in how they use their rewards.
3. Interoperability and expanded value
   Traditional loyalty programs, in most cases, limit rewards to a single brand or ecosystem. On the other hand, Web3 loyalty tokens function as universal currencies. This enables global redemption networks — permissionless collaboration through smart contracts and cross-sector partnerships.
4. NTF-based loyalty rewards
   Instead of receiving generic points, a customer is issued an NFT token. The uniqueness of NFTs adds a layer of desirability and collectability, making the loyalty program more engaging and valuable. The NFTs can be potentially traded or sold on secondary marketplaces, adding more value to customers who can turn their loyalty tokens into liquid assets.
5. Community driven engagement
   Web3 loyalty programs offer a community-centered approach through shared goals, collective rewards, and member governance through DAOs. By encouraging peer interaction it creates a sense of belonging, shifting focus from individual transactions to collective engagement.
6. Transparency and trust
   Blockchain infrastructure provides immutable transaction records and enhanced security. Real-time reward tracking is also possible through blockchain technology. This addresses consumer concerns about traditional programs’ security risks. It also builds trust and encourages more engagement.
7. Reduced unused rewards
   Web3 programs can implement “tokenomics” to prevent the devaluation of rewards and encourage active participation.

Navigating the Web3 landscape

While there is immense potential to build deeper customer connections with Web3, there are some considerations to help businesses approach this landscape strategically.

• Understand your customers
  Before adopting the Web3 loyalty programs, a business must understand its customers. It is important to find out if they are receptive to these technologies, as well as their digital habits and preferences.
• Start small
  Beginning with a pilot project and gradually integrating Web3 elements allows for learning and proper adaptation.
• Focus on value creation
  The key to success when adopting any new technology is providing genuine value to customers. The technology should enhance the customer experience.
• Educate customers
  Educate customers about the new adoption and provide clear guidance on how to interact with the technology.
• Stay informed
  The Web3 landscape is rapidly evolving; therefore, it is crucial to stay informed on the latest trends and best practices.

Conclusion

Web3 presents a unique opportunity for businesses to revolutionize loyalty programs through blockchain, NFTs, and decentralized engagement. The ability to prioritize personalization, security, and true ownership will help businesses develop deeper customer connections. Although Web3 might seem complex, the potential benefits for businesses that embrace this evolving technology are significant.

Know to Stay Legal

Artificial intelligence (AI) tools are reshaping content creation. It is now easier for businesses to produce images and videos for use on websites, social media, and other digital outlets. All this is possible without the traditional hurdles of expensive photoshoots, special design skills, or complex video production. However, as exciting as it is, business owners must pose and confront the question of whether these AI-generated images and videos are legally safe for commercial use from a copyright perspective.

Understanding AI-Generated Content and Copyright

AI-generated content is created by training algorithms with massive datasets of existing images, videos, and text. The AI models then analyze patterns from the training data to generate new content. However, issues arise concerning the ownership of the generated content. Without clear legal guidelines, the ownership of AI-generated images and videos remains a gray area that leaves businesses and individuals vulnerable to potential disputes.

Most jurisdictions, including the United States and the EU, deny copyright protection to work purely generated by AI as it lacks human authorship. The U.S. Copyright Office stated that only content with human creative input can be eligible for protection. In its January 2025 report, the U.S. Copyright Office also states that copyrightability must be assessed on a case-by-case basis.

Laws differ globally. For instance, while the U.S. copyright office has rejected applications for AI-generated content, the U.K. allows copyright when a significant human intellectual effort guides the output.

Copyright laws do agree that a business risks infringement claims if AI-generated content resembles existing copyrighted material. So far, there has been a surge in the number of copyright lawsuits because of generative AI. A good example is Getty Images sued Stability AI, alleging its Stable Diffusion model copied millions of Getty’s photos without permission.

Generally, despite the efforts made to develop copyright laws for AI output, unlike content created by humans, there still lacks a clear legal framework for ownership and usage rights. For one, laws and legal frameworks struggle to keep up with the speed at which AI technology advances. This means that currently, no definitive, globally recognized legal standards firmly establish the copyright status of AI creations. For a business, although using AI visuals is not inherently legal or forbidden, it is best to be cautious and take due diligence.

Best Practices Every Business Owner Must Keep in Mind

1. Read the terms of service (TOS)
   Every AI image and video generator has its own unique terms of service. Therefore, it is crucial to examine these terms carefully. Specifically, look for clauses that address issues such as commercial usage, ownership, indemnification, and TOS change policies.
2. Understand model releases
   This especially applies where the AI-generated images may include recognizable human faces. In the same way that there are rights of publicity and privacy in traditional photography of human models, consider if this also applies to AI-generated faces.
3. Documentation
   It is crucial to keep a record of each generated AI visual asset. Keep information such as AI platform used, prompts used, date of creation, TOS at the time of creation, and modifications made to the generated visual.
4. Consider using well-established platforms.
   Although there is no AI platform that offers a 100 percent guarantee of copyright safety, it is safer to lean toward well-established and respected AI generators. Also, platforms trained using licensed or public domain data should be considered.
5. Adopt the “human-in-the-loop” approach.
   This involves edits such as text overlays, color adjustments, or storyboarding. AI-generated content can be used as a starting point or for inspiration, but it is modified and refined by human designers. This results in a blend of AI assistant and human creative input to potentially mitigate copyright concerns.
6. Seek expert legal counsel.
   When dealing with content that is central to a business identity, such as branding or major marketing campaigns, it is critical to seek guidance from an attorney specializing in intellectual property law.
7. Stay informed
   Copyright law in the age of AI is not static; it is actively evolving. It is important, therefore, to commit to staying informed about legal developments, court rulings, and evolving practices. Business content strategies and practices also should be adjusted as the legal landscape changes.

Embrace the Future of Visuals Responsibly and Legally

The transformative power of AI to generate stunning visuals is promising to revolutionize business marketing and communication. However, business owners must approach this technology with a balanced perspective. That is, embracing its potential while avoiding copyright infringement, ensuring ethical content creation, and effectively safeguarding intellectual property assets.

The rise of artificial intelligence (AI) is continuously transforming how businesses operate, offering opportunities for efficiency, innovation, and growth. However, in an increasingly competitive landscape, businesses seek solutions tailored to their specific industries. To meet this demand for more tailored tools, vertical AI agents are emerging as key to staying ahead in the age of specialization.

What are Vertical AI Agents?

Vertical AI agents are designed to solve specific problems within industries in areas such as finance, retail, and healthcare. This differs from horizontal AI, which provides general capabilities across various sectors. Horizontal AI cross-functional applications such as marketing automation are applicable across different sectors. These horizontal AI solutions were witnessed in the early days of AI, when companies like Google, Microsoft, and Amazon created broad AI solutions. These solutions handle multiple tasks but are not optimized for any specific ones.

Vertical AI has been enabled by advancements in Large Language Models (LLMs), which now possess the capability to process complex, industry-specific data and automate complex tasks. These breakthroughs and the inefficiencies of outdated technologies in many industries have created a demand for specialized solutions. Additionally, some platforms simplify the creation and deployment of vertical AI by providing data management and customization tools. At the same time, businesses increasingly recognize AI’s potential to drive efficiency and competitive advantage.

Vertical AI agents are emerging as the next disruption in tech and are anticipated to dominate in 2025. With its market valued at $5.1 billion in 2024, the figures are projected to rise to $47.1 billion by 2030.

Some areas where vertical AI agents are used include finance to enhance risk assessment models and provide insights into market trends and investment opportunities.

Banking institutions are also deploying vertical AI agents to detect fraud in real-time and reduce manual intervention.

In retail verticals, AI agents help personalize product suggestions for customers.

It is important to note that the success of vertical AI precision depends on its ability to solve clear and specific problems. It leverages industry-specific data and domain expertise to deliver solutions that have better precision than general AI systems.

As such, some companies have begun building their own AI tools by using their datasets to create tailored solutions for their specific industry challenges.

Key Benefits of Vertical AI Agents for Niche Markets

1. Increased operational efficiency – Frees human resources by automating complex and repetitive tasks to increase productivity. Employees have more time to focus on tasks that require creativity, strategy, or problem-solving.
2. Enhanced accuracy and decision-making – Vertical AI agents are trained on vast amounts of industry-specific data. As a result, they deliver more accurate and consistent results. This reduces human error, which may have dire consequences in critical high-stakes fields such as healthcare and finance.
3. Cost saving – automating tasks traditionally performed by large teams helps reduce costs. It lowers payroll expenses and minimizes operational costs. This enables companies to reallocate resources to innovation and growth rather than to routine tasks.
4. Unlock new markets – traditional software solutions may struggle to penetrate niche markets. This is because of their complexity or unstructured data requirements. However, vertical AI agents handle these challenges effectively, opening up new revenue opportunities in previously underserved segments.
5. Improved customer experience – vertical AI agents enhance customer interaction since they can provide personalized service and faster response time.
6. Competitive advantage – businesses leveraging vertical AI agents have a significant competitive edge over competitors relying on generalized solutions.
7. Driving innovation – vertical AI agents streamline operations and offer data-driven recommendations. This enables businesses to experiment and develop cutting-edge products and services. Ultimately, a business can maintain a competitive edge in niche markets.

Challenges and Considerations

Vertical AI agents have compelling benefits but also come with some challenges. A business must navigate the potential challenges during implementation. This includes integration with existing systems, data privacy concerns, employee resistance, and the need for ongoing human oversight. The good news is that with careful planning and a strategic approach, it is easy to overcome these challenges and fully realize the benefits of vertical AI.

Closing Thoughts

Automation has become a critical tool for businesses that want to remain competitive. As the demand for smarter and more efficient operations rises, vertical AI agents are emerging as a solution. These advanced AI solutions deliver targeted results by focusing on niche applications. As AI continues to advance, vertical AI agents will become more efficient and accessible, integrating with broader systems.