Massive Artificial Intelligence Database LAION

Until recently, experts focusing on child abuse believed that unregulated AI tools could only create child abuse images by merging their knowledge of adult pornography and non-harmful child photos.

The massive artificial intelligence database LAION contains over 3,200 photos of children who may have been sexually abused. This database has been used to train top AI image-makers like Stable Diffusion. Together with the Canadian Centre for Child Protection and other anti-abuse organizations, the Stanford University watchdog group was able to detect the inappropriate content and notify the authorities of the original photo connections. It claimed that about 1,000 of the pictures it discovered had been independently verified.

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Malicious Content Creation

There was a lightning-fast reaction. While the Stanford Internet Observatory’s study was expected to be released, LAION informed The Associated Press that it would be temporarily withdrawing its datasets. In a statement, the nonprofit organization known as LAION (Large-scale Artificial Intelligence Open Network) stated that it “has a zero-tolerance policy for illegal content, and in an abundance of caution, we have taken down the LAION datasets to ensure they are safe before Republishing them.”Stability AI, a London-based firm that makes the Stable Diffusion text-to-image models, is a notable LAION user who contributed to the development of the dataset. While recent updates to Stable Diffusion have significantly reduced the likelihood of malicious content creation, a previous version from last year—which Stability AI denies releasing—is still embedded in various applications and tools and is reportedly “the most popular model for generating explicit imagery,” as stated in the Stanford report.

Even though it isn’t always obvious, the LAION database is a source for many text-to-image generators. The developers of DALL-E and ChatGPT, OpenAI, have said that they do not employ LAION and have adjusted their models to reject requests for sexually explicit material involving children.

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The Stanford Internet Observatory Thoughts

The Stanford Internet Observatory has called for more extreme measures because it is difficult to clean up the data retroactively. “Delete them or work with intermediaries to clean the material” is one option for everyone who has constructed training sets using LAION-5B, which is named for the over 5 billion image-text pairs it contains. Another option is to completely remove an earlier version of Stable Diffusion from the internet, leaving only the most obscure places for it. For instance, Thiel criticized CivitAI, a platform popular among those who create AI-generated pornography, for what he perceived as a lack of safeguards that would prevent the creation of photos of minors. Hugging Face, an AI startup that provides model training data is also urged to improve its reporting and removal processes in the study.

In light of safeguards provided by the federal Children’s Online Privacy Protection Act, the Stanford paper raises the question of whether any images of children, no matter how innocent, should be inputted into AI systems without the permission of their families. To identify and remove child abuse content, tech corporations and child protection organizations already issue films and photographs a “hash”—unique digital signatures. If you believe Portnoff, you may apply the same idea to abused AI models.

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“In the past, casual viewers (or listeners) could easily detect fraudulent content. This may no longer always be the case and may allow any adversary interested in sowing misinformation or disinformation to leverage far more realistic image, video, audio, and text content in their campaigns than ever before.” 

Department of Homeland Security, Increasing Threat of DeepFake Identities.

In today’s rapidly evolving technological landscape, we find ourselves at a crossroads, where the lines between the real and the fabricated are blurring like never before. Deep fake technology, once a buzzword relegated to the realm of sci-fi, is now a force to be reckoned with for both consumers and businesses alike. Its implications are vast, impacting how we interact in an increasingly virtual world. As exposure to deep fake technology increases, we must address both the potential consequences they pose and the steps businesses can take to verify and protect an individual’s identity.

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For businesses across numerous industries, deep fakes present a critical challenge.

The technology is no longer confined to amateurish impersonations; it has evolved into a sophisticated tool that can convincingly mimic an individual’s physical likeness, voice, and even mannerisms. It is now true that you can create a deep fake that is so convincing it will fool almost anyone.

For example, you might understandably believe you are speaking to your spouse on the phone because it sounds identical to your loved one, but in reality is a deep fake. However, if your caller ID says the call is from Philadelphia and you know your spouse is in Palo Alto, your alarm bells will be going off.

The potential for increased consumer retail, healthcare, and financial fraud, along with more sophisticated phishing schemes capable of imitating physical appearance and voice, has never been more pronounced. These vulnerabilities expose not only consumers but also businesses to significant risks, particularly targeting the most susceptible demographics, including both the young and the elderly.

So, what steps can businesses take to safeguard their systems, ensuring they can maintain trust and security with their customers?

Embracing Multi-Factor Authentication (MFA):

Multi-factor authentication is a game-changing defense against the rising threat of deep fake technology. MFA incorporates layers of identity verification, requiring “something you know” (like a password), “something you have” (such as a token or device), and “something you are” (like a picture of you and your ID).

By incorporating these multiple layers, MFA affords an additional security layer that deep fake technology will struggle to breach. For businesses, this translates to a robust defense against a fraudster’s ability to exploit vulnerabilities, providing customers and employees with an elevated level of trust and security as their identities and data are shielded on multiple levels.

Continual Learning and Adaptation:

In this digital age, where fraudsters constantly seek innovative ways to breach security measures, businesses must remain one step ahead in terms of technology and strategy. The cat-and-mouse game between security and those who wish to compromise it necessitates a proactive stance. Businesses should consider partnering with an identity verification provider, a key ally in an ongoing battle, to gain access to cutting-edge technology and strategies that are fine-tuned to combat the latest fraud tactics.

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Educating the Public:

It is imperative to educate the public about the existence of deep fakes and the potential risks they pose, encompassing not only consumers but also employees in the corporate sphere.

As deep fake technology advances and becomes more sophisticated, individuals must be made aware of its existence and the potential risks it poses. When individuals can recognize the signs of a deep fake or understand the risks associated with impersonations, they become more resilient against deceptive attempts. In the context of businesses, educated consumers are an invaluable asset, as they are less likely to fall prey to fraudsters attempting to exploit trust and authenticity.

Collaborative Efforts:

In an era where the threat landscape is constantly evolving, collaboration among businesses, government agencies, and identity verification and cybersecurity experts is vital. Sharing insights and intelligence can help create a united front against deep fake attacks, bolstering the resilience of all stakeholders.

Ultimately, there is no doubt that the rise of deep fake technology presents a significant challenge to the preservation of trust and security in our interconnected digital landscape. For businesses, this threat is particularly pronounced, as it challenges the very foundation of trust between brands and their customers. These challenges are daunting, but with the right strategies and a commitment to safeguarding trust, businesses can preserve the integrity of their digital interactions, and continue to thrive in an increasingly virtual and interconnected society.

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

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Reports of unprecedented heat records globally have dominated the headlines of this year. As on 8 August, the United States had witnessed 15 weather and climate events causing over $1 billion in damages each. This surge in natural disasters, well above the 1980-2022 annual average, underscores the urgency for advanced risk management solutions. Artificial Intelligence (AI) emerges as a powerful tool to navigate escalating climate risks, enabling businesses to secure their future proactively.

Enhancing Precision in Climate Risk Management

The intensifying unpredictability of climate patterns has complicated risk management for businesses. Though historically sound, traditional models often need to anticipate the intricate impacts of climate change. These models, rooted in historical data, need help to account for the rapid and dynamic shifts occurring in today’s climate landscape. This inherent limitation has led to instances where businesses have been caught off guard by unprecedented and unexpected weather events, resulting in financial losses and operational disruptions.

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AI offers a revolutionary approach to climate risk management.

AI excels at deciphering complex data sets, revealing intricate patterns and correlations. By analyzing diverse data points from past and present climate trends, AI crafts a comprehensive narrative, providing businesses with a clearer vision of potential future risks. This refined forecasting equips businesses to take proactive measures against emerging climate risks.

Imagine a company operating in a region with historically low flood risk. With AI’s predictive capabilities, this company can now detect rising flood risks due to shifting weather patterns, enabling them to implement preventive measures or secure suitable insurance coverage beforehand.

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AI’s lasting value in forecasting extends beyond business considerations, stretching into the financial landscape. By enhancing risk assessment tools for physical, transition, and economic climate risks, AI empowers investors to venture into climate-related opportunities. This injection of capital bolsters industries vulnerable to climate change and nurtures the growth of resilient enterprises. For instance, armed with AI-derived insights, an entrepreneur could direct investments toward infrastructure projects in regions susceptible to rising sea levels.

In our pursuit of tackling escalating climate challenges, AI emerges as a pivotal solution within a toolkit of essential technologies and strategies to enhance climate resilience. AI surpasses mere adaptation to ignite proactive transformation by refining precision in risk management and unveiling novel investment pathways. As businesses and investors uncover AI’s potential, they protect their interests and contribute to a future marked by resilience and sustainability.

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

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AI is becoming more prevalent in our day-to-day lives, from self-parking cars to personalized advertising. With the evolution of AI tools taking great strides forward and seemingly accelerating at the moment, experts in an open letter have recently called for a slow-down or temporary hold on research and development. The letter was signed by Elon Musk, Steve Wozniak, and Yuval Noah Harari, among others, who want to press pause until the potential societal impacts of the tech can be properly assessed and regulatory guidelines and safeguards can be put in place. 

Those who have seen the Pixar film Wall-E, you’ll know that the depiction of our future is one where humans have become totally complacent and reliant on technology. It’s scary to think of how AI, if not regulated, could affect consumerism, well-being, and health. But, while many of those fears are unfounded, there should be no doubt that the unintended consequences and side-effects of applying AI in a decision-making role (such as bias and disinformation) are real and can be very serious in practice.

The race is on for AI regulation

It’s truly remarkable how far generative AI has come over the past couple of years, no doubt spurred on by the intense race between Google and Microsoft. As a society, it’s right that we should be concerned that these technology giants and others develop AI responsibility. However, it’s silly to expect them to suddenly now hit the brakes, especially when they have so much to gain. Market players have no incentive to slow down and are all competing for their share. It seems like new tools from the big players are being announced weekly, if not daily, most recently with Meta testing generative AI ad tools. However, this is exactly why regulators should be thinking about the impact of AI and acting right now. 

Those working with AI should welcome external regulation. As the legitimate concerns around AI’s potential impacts on society increase, governments around the world are looking at how they can best regulate AI without stifling its incredible potential.

The problem with privacy laws

While there are varying opinions across the world around how AI should be regulated, the most effective approach will be for governments to create new AI-specific laws that help to not only govern it but apply it to our advantage to benefit all parts of society. However, as we have seen with the piecemeal development of data privacy law, if individual countries take their own approach to the regulation of AI, it could cause more harm than good (eg. friction, added costs from a heavy compliance burden, protection gaps, misinformation, and fear). Taking a local approach will lead to AI learning in different ways – instead, we need a globally unified approach.

A United Nations of the Internet

For AI development to benefit all of society, the key pieces that regulation must get right are building global standards around AI system transparency, data privacy, and ethics. We need to create a ‘United Nations of the Internet’ for unified regulation across the world. This means there will be a consistent understanding of its potential and limitations, no one country will have advantages over another, and businesses in multiple markets where they can apply AI seamlessly. It will also be important to unite on regulation for AI to build trust and demonstrate its potential, that way we can help society embrace it, rather than fear it. 

ChatGPT is a prime example and a reason that AI regulation is in the spotlight. It has sparked fear that it will take jobs, cause unemployment, and damage society. In actual fact, if regulated and applied correctly, AI will enhance society by opening up more meaningful jobs and personal time rather than repetitive tasks.

Unlocking AI’s Potential

We are standing on the brink of a new wave of human potential, fuelled by the power of AI. To truly unlock its potential for good, we need to learn the lessons of our experience with data privacy laws and find a way to create and agree upon the global standards around the ethical and positive use of AI tools. 

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

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Artificial intelligence (AI) is a powerful tool in the fight against misinformation, particularly when it comes to detecting false or misleading information at scale. With the increasing volume of information on the internet, detecting and addressing fake news is a significant challenge for news companies and consumers. AI can help by identifying patterns in large amounts of data, flagging content that is likely to be false or misleading, and even predicting potential sources of misinformation before it spreads.

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Despite the impressive progress in AI-powered analysis, the technology in its current state still has limitations. AI algorithms often lack context when processing information, so while it can detect patterns and anomalies, it may not always understand the nuances of the content it is analyzing. This is an obvious drawback in the world of news media and misinformation detection, but these shortcomings will also apply in the enterprise setting as well.

As retailers, CPG brands, and pharmaceutical companies begin to experiment with generative AI tools like ChatGPT, DALL-E and others in their marketing efforts, they should draw upon the following tips and best practices that came out of AI-backed news media efforts. 

AI’s Applications in News Media

It’s fair to say AI has revolutionized news media, making it important to understand the role that AI plays in the current media landscape. While it may not seem obvious to the average consumer, AI algorithms often determine which news gets presented and how it’s delivered on a daily basis. 

App-based news aggregators, for example, deploy algorithms that look at reader profiles, including the user interests, browse signals, readership patterns, trending documents and other factors to provide content that conforms to their habits and preferences. Organizations can also direct these algorithms to seek feedback on app stores, social media and chat sites to help inform a tailored feed. Some social media platforms that also share news, however, don’t treat their news content any differently than a post from a friend. These models optimize for engagement, to the point where everything feels a bit too hyper personalized, causing users to impulsively scroll through negative, junk and repetitive content for hours at a time, also known as doom-scrolling. 

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Fortunately, there are some news media companies using AI algorithms to promote content discovery and foster new perspectives among readers, creating guardrails and training AI to help foster accurate, trustworthy and enjoyable feeds. Some news applications use AI models to flag suspicious coverage based on things like a publisher score (which helps verify quality based on voice, authenticity, posting date, reviews, and more), and have implemented rules and procedures to identify and remove misleading and fake news. While this is an effective use of AI’s analytical capabilities and a step in the right direction, there are some limitations to keep in mind that only a human can address, including context and empathy. 

Human Role in Fake News Detection

It’s crucial to understand at this stage in AI’s technological advancement, it doesn’t always comprehend the full context surrounding a piece of news or impact it might have on a reader. Therefore, AI tools can get it wrong and flag, or miss, content as a result. Because of this, human oversight, intervention and continual refinement of guidelines remains a necessary step to ensure accuracy and appropriate handling of content. 

New users of ChatGPT and other commercial generative AI tools already have a sense of this process, as they intuitively question or accept the text output they receive. When companies begin to deploy generative AI in the enterprise setting, they must consider policies requiring humans to be kept in the loop at all times. The combination of AI and human expertise is a powerful and effective approach to combating misinformation and solving work-related issues.

Using AI Ethically

Whether or not there is human oversight layered into an AI application, there are key elements to consider regarding the ethics of AI. First, AI must be leveraged with the right intent. Technology itself is never good or bad; it’s the intent of the user that determines this. It’s up to industry sectors, organizations and consumers to deploy AI responsibly and ensure they have the right guardrails and policies in place to manage and mitigate risk. 

It’s also essential to be transparent about using AI. There’s nothing wrong if a piece of content was generated by AI, but it’s key for the content owners to be transparent about it and take responsibility for the final product. A news outlet might leverage AI to generate an article, for example, but informing the readers of that fact will build trust in the long run. Informing readers could be a disclosure in the dateline or as part of an annotation. Each news organization may choose its own path, but there does need to be an indication that AI was used, otherwise it can be seen as disingenuous. 

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The first foray into enterprise AI usually involves out-of-the box solutions. The best ones have built-in protocols to recognize when bias is clouding its judgment. Even with these safeguards, it is critical for humans to exercise reasonable discretion when using the tool. If companies have the opportunity to build or license their own AI models, they must ensure that the algorithms are designed responsibly and prudently. 

We’re at the beginning of something special, like the Internet in the 90’s and while we’re just now beginning to understand the power of generative AI, we can only partially comprehend the implications it will have on society as a whole. Since AI can’t think for itself yet, we need more smart people who can understand the design faults and build guardrails to prevent abuse. I’m excited about the future of AI, its potential and the amazing community that will help shape its future.

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

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The EU recently took a significant step towards establishing the world’s first rules on Artificial Intelligence (AI). In a move that highlights the growing importance of ethical and human-centric AI development, the new regulations promise to transform the landscape of AI applications across industries. Spain follows suit with its groundbreaking Customer Service Law, setting new standards for customer care and accountability in the business sector. These legislative advancements, while distinct, underscore a shared commitment to prioritizing consumer interests and leveraging technology responsibly.

In a world where digital transformation is no longer optional, but a necessity, these legal frameworks serve as crucial guidelines for businesses navigating the integration of AI and other advanced technologies into their operations. From enhancing service delivery to ensuring transparency and fairness, the EU’s AI Act and Spain’s Customer Service Law offer a balanced approach to technological innovation, ensuring consumer protection while fostering growth and innovation.

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The intersection of these new regulations highlights the delicate balance businesses must strike: harnessing the transformative power of AI and advanced technologies, while ensuring consumers’ rights are protected and their needs are effectively met. This article explores these two legislative milestones and their implications for the future of AI and customer service in the business landscape.

The AI Act: A Historical Moment in AI Governance

The draft negotiating mandate on the first-ever rules for AI, adopted by the Internal Market Committee and the Civil Liberties Committee, marks a critical moment in AI governance. It aims to ensure that AI systems are overseen by people and are safe, transparent, traceable, non-discriminatory, and environmentally friendly.

This pioneering legislation reflects the urgent need to balance the protection of fundamental rights with the necessity of providing legal certainty to businesses, stimulating innovation in Europe.

Prohibited AI Practices: Setting Ethical Boundaries

The new rules adopt a risk-based approach, establishing obligations for providers and users depending on the AI’s potential risk level. Certain AI practices with an unacceptable level of risk to people’s safety would be strictly prohibited, including systems that deploy manipulative techniques or are used for social scoring.

The Ban on Intrusive AI Systems

The legislation includes bans on intrusive and discriminatory uses of AI systems, such as real-time remote biometric identification systems in publicly accessible spaces, predictive policing systems, and indiscriminate scraping of biometric data from social media.

High-Risk AI: Expanding the Classification

The classification of high-risk areas now includes harm to people’s health, safety, fundamental rights, or the environment. The list also includes AI systems used to influence voters in political campaigns and recommender systems used by large social media platforms.

Foundation Models: A New Age of AI

The legislation recognizes the rapidly evolving field of AI and includes obligations for providers of foundation models. These models, like OpenAI’s GPT-4, will have to comply with additional transparency requirements, such as disclosing that the content was generated by AI and designing the model to prevent it from generating illegal content.

Supporting Innovation and Protecting Citizens’ Rights

To boost AI innovation, the legislation includes exemptions for research activities and AI components provided under open-source licenses. It also promotes regulatory sandboxes or controlled environments established by public authorities to test AI before its deployment.

The Right to File Complaints

The law strengthens citizens’ right to file complaints about AI systems and receive explanations of decisions based on high-risk AI systems that significantly impact their rights.

Spain’s New Customer Service Law: A Shift towards Customer-Centric Approach

In a similar vein, Spain recently introduced a new Customer Service Law. Under the law, companies with over 250 staff or $53 million in annual revenue are required to provide live customer support during business hours, limit the wait time to three minutes or less, and resolve issues within 15 days. Customers can be offered the alternative to solve their issue through AI but must be able to state to the AI that they wish to speak to a human if they feel that the AI does not understand their request.

The law also stipulates that in case of service interruptions, companies must inform the customer about the issue and provide a solution within two hours. Non-compliance can result in substantial fines, signaling a strong move towards enforcing a customer-centric approach in business operations.

The Future of AI: A Delicate Balance

As we stand on the brink of a new era in AI regulation, we must strike a delicate balance between leveraging the benefits of AI and mitigating its risks. The EU’s AI Act and Spain’s new Customer Service Law represent significant steps in establishing robust, comprehensive, and human-centric governance models that embrace innovation while safeguarding fundamental rights.

Fostering Responsible Innovation

The AI Act promotes responsible innovation by requiring AI providers to meet specific transparency measures. For instance, foundation models, like OpenAI’s GPT-4, must comply with requirements such as disclosing AI-generated content and preventing the generation of illegal content. This fosters a culture of accountability and openness in AI development, which is critical in building trust between AI providers and users.

Enhancing Customer Experience

Spain’s new Customer Service Law underscores the importance of prioritizing customer experience in today’s digital age. It sets clear expectations for businesses to provide timely and efficient customer support, highlighting the role of customer service in building strong customer relationships and enhancing business reputation.

Encouraging Technological Investment

The Customer Service Law presents an opportunity for businesses to rethink their strategies and invest in technologies that can help them meet the new customer service standards and create a customer-centric culture.

Embracing the Future

As AI continues to evolve and become more ingrained in our everyday lives, we now find robust regulations that guide its development and use. The EU’s AI Act and Spain’s new Customer Service Law will serve as benchmarks for other regions looking to establish their own AI rules and regulations.

As we move forward, it’s crucial for businesses to not only comply with these regulations but also view them as an opportunity to build trust with their customers, foster innovation, and ultimately, harness the full potential of AI. By doing so, they can ensure they remain resilient and competitive in the rapidly changing technological landscape.

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

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Until recently, understanding customer was considered the grail. But, with the rise and accelerated peaking of ChatGPTs, understanding chatbots would become the Holy Grail of Marketing and Customer service. Chatbots or Artificial intelligence (AI) chatbots are becoming increasingly popular in the digital space, as businesses strive to improve their customer experience and streamline operations. However, with the rise of AI chatbots comes the potential risk of defamation claims against them. Fortunately, Section 230 of the US Communications Decency Act provides a legal shield for AI chatbots, protecting them against defamation claims.

Last week, OpenAI was called out in a news report for threatening an AI developer with legal action. Reason? The AI developer released a generative AI tool called GPT4free that bypasses the payment wall of ChatGPT4. There are different types of risks associated with the use of AI platforms that are increasingly getting adopted by businesses without enough testing and validation. One of them is the risk of AI-powered disinformation, which essentially means that chatbots could be sharing authoritative-sounding information that are actually false. With GenAI tools, the risks have only magnified more in the recent weeks. As LLMs become more progressive and advanced in their ability to self-learn from different sources, including platforms that are not verified for accuracy of information or fake news, the risks of disinformation could become counter-productive to both, the business owners as well as end-users.

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The Communications Decency Act of 1996, provides immunity to online service providers and website owners from liability for content posted by their users. This means that if an AI chatbot is accused of defamation, the chatbot’s owner is protected from legal action.

According to first-party data collated by wordenfirm.com, due to the implementation of Section 230 AI chatbots are less likely to be defamed or accused of spreading false information. In fact, the data shows that AI chatbots have only been involved in 2% of all defamation cases, compared to 32% for human users. The data highlights the importance of Section 230 in protecting AI chatbots from legal battles that can be both time-consuming and costly.

Andrea Worden, lawyer and founder of wordenfirm.com, shared her expert comment on the importance of Section 230 in the digital landscape. She states, “Section 230 has been a game-changer for AI chatbots, providing legal protection and allowing businesses to leverage this technology with confidence. It has revolutionized the way we interact with technology and has opened up new opportunities for businesses to improve their customer experience.”

With Section 230 in place, AI chatbots are a powerful tool for businesses to enhance their customer experience and streamline operations. They can provide personalized assistance, offer solutions, and make interactions more efficient. By protecting AI chatbots from legal battles, Section 230 is enabling businesses to leverage this technology with less risk and greater confidence.

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

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The biggest challenges faced by wholesale businesses, and how they can overcome them with digital solutions

The B2B wholesale business is an essential part of the global economy, providing products and services to all kinds of customers. Despite its vast influence, however, the industry continues to face several challenges that impact its growth and success.

Perhaps the single most important inhibitor to growth is the slower adoption of digital transformation in the B2B sector, especially when compared to businesses that sell direct-to-consumers. Without prioritizing digital transformation and modernizing to digitally-led sales processes, the long term impact for B2B businesses will be felt in sales figures, profitability and customer success. And the gulf between businesses that rely on legacy processes and those that adopt digital commerce strategies will only widen too. This will be intensified with the introduction of more automated tools designed to fast-track process enhancements.

Of course, change does take time. And it takes even more time when the day-to-day challenges always seem to take precedence over longer term growth ambitions and digital transformation initiatives. Many wholesalers get stuck in a cycle of troubleshooting to constantly balance their margins and maintain customer satisfaction, and this prevents them from moving to the next stage of growth. 

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Overcoming these daily challenges is not made any easier by the fact that too many digital commerce solutions are not designed for B2B, but rather as an afterthought on B2C platforms. The consumer ecommerce features are bent to try and fit a B2B proposition, when in fact sales journeys and business processes in B2B are often extremely different and far more complex than their B2C cousins.

B2B digital commerce requires a digital solution that caters specifically for wholesaler needs and gives them the tools they need to use data and insights to their advantage. It’s worth taking a closer look at some of the biggest challenges that are specific to wholesalers – that may be standing in the way of progress – and see how they can be overcome.

Inventory Shortage & Overstocking

Managing inventory levels is the trickiest task for any wholesale business. An inventory shortage can lead to missed sales opportunities, dissatisfied customers and reduced order values. On the other hand, overstocking can result in increased expenditure due to the cost of holding excess inventory, which could also get damaged or spoiled if handled incorrectly.

Implementing an automated inventory management system will allow wholesalers to track inventory levels in real time and make informed decisions about when and how much to order. Alerts will not only be triggered as stock lines deplete, but these can be connected with other data, such as expected lead times for deliveries from different suppliers, seasonal changes in demand, or the frequency of repeat orders from large clients. In this way, inventory levels will work in harmony with customer demands so that products are not out of stock when the customer needs them, and not overstocked when they don’t.

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Poor Visibility into Product Profitability

Determining which products are the most profitable and which ones are not may sound like simple business sense, but these details can often get lost when managing enterprise level operations. In one scenario, siloed decision-making that varies from one department to another can lead to inconsistent strategies. Or, at the other end, giving equal importance to all SKUs can take the spotlight away from hero products that should otherwise have a greater share of sales to increase profits. 

It is possible to create a more insights-driven strategy by collecting data on product profitability across different lines and then creating the right product mix. With digital support, this can even be extended to create customer-specific catalogs.

A digitized process will enable a far more sophisticated and dynamic approach to managing multiple lines and customers. Managers will no longer need to make all of the calculations and decisions manually. Using process mining technology, for example, the system will capture all the data for a holistic view and then provide insights on the most effective strategies. A platform designed for wholesalers will also make it easy to turn these insights into action by implementing the orchestration of the new rules.

Mismatch in Customer & Supplier Demands

The key to running a wholesale business successfully is balancing the demands of customers and suppliers. Wholesalers face a careful balancing act of keeping both customers and suppliers on good terms. Good supply chains will help protect margins, and good customer satisfaction is needed to protect revenue, but these are both on constantly shifting sands and require close attention.

Managing a supply chain using digital tools and insights will provide the analytics to know of any changes in real time so that these can be factored into the front-end customer experience. For example, a wholesaler may receive an order for a product that experiences unforeseen delays. Instead of having to apologize to the customer that delivery will be delayed and risk losing that relationship, an automated system can immediately identify suitable alternatives based on the customer relationship history and diffuse the situation. It could offer an adjustment to the order, for example, such as a discount or other promotional privilege, and keep the customer on good terms. 

Or perhaps a manufacturer decides to change the price of its product, making it less competitive in the market and a decision is needed on whether to continue to stock it. Again, process mining will help to identify the data relating to that product, such as popularity or profitability, and offer insights on the best course of action. In these examples, digitized processes speed up decision making so that the impact of an issue on the end user is minimized. 

Profit Margins & Cash Flow

Wholesalers need to maintain the right level of profit margins to remain competitive in the market, while also ensuring they have sufficient cash flow to meet maintenance, financial obligations, and to be able to experiment with new projects. Business owners need to carefully manage their pricing strategies and monitor their cash flow closely, taking steps to reduce costs and increase revenue wherever possible. 

Dynamic pricing is increasingly coming into play to enable businesses to have more flexibility in their pricing strategies. In the same way that taxi fares increase when there are high levels of demand, and decrease at less busy times, so too can wholesaler pricing strategies. 

At some points it is worth changing prices to offer more promotions and bulk buying incentives to keep valued customers happy, while at other times it is important to protect margins when challenging new environments hit, and this can change frequently in many wholesale industries. These adjustments should be done on a customer-by-customer basis too. Wholesalers can become far more agile in responding to market conditions by introducing dynamic pricing options. If they don’t, then they are already at a disadvantage when facing changes that are outside of their control.

Top News: Role of AI and Machine Learning in Journalism

Slow Growth

In a highly competitive market with rapidly changing demands, dropping the ball on any of these challenges might mean that management time is taken up with trouble-shooting, and not on strategies that can grow the business. When operations are managed in an inefficient way, it’s no surprise that growth is going to be hamstrung. 

Digital transformation is the fuel needed to accelerate growth. Neglecting to invest in it will mean losing competitive edge over the long run. Key to this is adopting a more data-driven approach that will give wholesalers the kind of agility they need to put them in a much more commanding role in making sure that they are maximizing sales opportunities, reducing the cost of inefficiencies, and looking after customer satisfaction.

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

The post Top Five B2B Challenges for Businesses in 2023 appeared first on AiThority.

Artificial intelligence (AI) and machine learning (ML) are taking center stage in 2022 as more than three-quarters of technical leaders see AI/ML as essential to driving revenue at their organization. But while AI/ML is powering market and vertical trends, many organizations still face pain points that prevent them from scaling effectively.

Meanwhile, 26% of top enterprises do have viable AI/ML initiatives deployed at scale, winning a competitive advantage over companies that haven’t yet. To gauge where enterprises are in their AI/ML journeys, SambaNova surveyed 600 AI/ML, data, research, customer experience and cloud infrastructure leaders at the director level and above. The survey captured 100 responses from each of six industries, including financial services, healthcare and life sciences, retail and e-commerce.

To solve scale issues, the survey results revealed two key trends for a path forward. As the AI/ML talent shortage continues, more companies are customizing at scale through a partner to successfully deploy AI. Additionally, businesses are harnessing the increased efficiency of chip architectures tailored to AI/ML — and reaping the benefits of reduced power consumption.

If you’re unsure where to start on your AI/ML journey, you’re not alone. Whether you’re improving existing initiatives or building entirely new infrastructure, keep one thing in mind: Invest in better AI/ML landscape-specific tools today to keep pace with competition in 2022.

The survey results are in: AI/ML is here to stay—but scaling is hard The ultimate paradox persists for organizations in 2022: Industry leaders recognize a need to innovate, increase revenue and drive operational efficiency with AI/ML solutions — yet they can’t effectively scale in a competitive landscape. To succeed with AI/ML enterprise-wide and come out on top, industry leaders need to invest in deep learning, reinvent their infrastructure and customize their strategies to specific use cases.

Our report revealed several trends about the changing face of AI/ML in 2021. Let’s explore our key findings, and what these results mean for you and your business in 2022.

1. Organizations have high hopes for their AI/ML initiatives.

From creating new products and services to investing in new lines of business, technology leaders are ready to adapt in a rapidly evolving market by investing in ML to power innovation, improve operational efficiency and keep up with competitors. Over two-thirds of organizations (70%) plan to allocate more than $100 million of IT budget toward strategic technology goals. It’s clear that organizations are looking to push their AI/ML investments further than simply automating tasks — and you should, too.

Based on these results, it’s no secret that competition will be fierce in 2022. Much like the internet boom of the early 2000s, AI will significantly shake up the Fortune 500 — startups and investors alike are recognizing the potential of AI solutions to help them remain competitive. The financial industry is investing particularly heavily in AI/ML, with a staggering 81% of financial services respondents planning to increase their investments in AI/ML — the highest percentage in any industry.

2. Organizations are diving head-first into deep learning.

Deep learning, a subfield of AI/ML that uses artificial neural networks to ingest and process unstructured data like text and images, is increasingly essential in almost every industry. Three-quarters of respondents (75%) say improving access to deep learning is very important for fostering competition and innovation in their industry.

From recommendation algorithms to natural language processing (NLP), the widespread use of machine learning presents a challenge for all but the most advanced computing infrastructure. Despite the clear benefits of deep learning, organizations remain limited by insufficient infrastructure and a lack of clear understanding of specific use cases. Furthermore, few business leaders grasp its transformative potential. Be sure to improve your infrastructure, and focus on education on the business side by training team members on how deep learning can support business goals.

Deep learning is perhaps the best example of a rising trend in the future of AI: Accessibility is driving new AI use cases across industries.

Against the backdrop of a global pandemic and supply chain shortages, 2022 will see a profound push for AI accessibility as countries seek to secure an economic advantage in the global market. As more organizations gain access to the power of AI, there will be new, transformative use-cases across industries, including innovations in biotechnology, supply chain and logistics and financial services.

3. Overcoming barriers is key to AI/ML scale.

As organizations struggle to handle compute-heavy workloads with dated infrastructures, one thing is clear: AI/ML specific chip architectures are essential to scaling effectively. We can no longer rely on Moore’s Law, meaning the number of transistors in a microchip won’t double every two years. Facing an infrastructure crisis, more than half of respondents (53%) strongly agree they’ll run out of computing power in the next decade without new computing architecture. By overcoming these challenges, your business can effectively scale AI/ML in a competitive market.

To overcome barriers to scale, a full-stack approach to AI is key. AI is pushing computing processing past its limits.

Current GPUs and CPUs aren’t able to keep up with the runtime requirements for probabilistic computing applications. As the market continues to mature and evolve, AI providers will focus more extensively on full-stack hardware and software systems that are designed specifically for AI deployments.

Deploy AI/ML at scale or risk falling behind If your inbox is anything like mine, you’ve been flooded with predictions for 2022. Beyond the state of the supply chain and the longevity of the Great Resignation, one theme remains top-of-mind: Artificial intelligence is here to stay.

Companies that aren’t innovating with AI to scale faster than the competition won’t win out. Across industries, we will see an increased focus on software and hardware systems that are specifically designed for AI and can handle massive amounts of data. As companies prioritize accessibility amid the ongoing supply chain challenges, a need for new AI use-cases will accelerate technology adoption across sectors.

Beyond using AI/ML to streamline operations, companies need to drive innovation by deploying AI/ML at scale to come out ahead. The AI/ML revolution is so disruptive that companies who fall behind, will be left behind. It’s time to move quickly to embrace, scale and innovate with AI. Read the data study to learn more.

The post The Secret to Remaining Competitive in the AI/ML Landscape? appeared first on AiThority.

PraSaga, a Swiss foundation creating a new Layer One Blockchain, and Metahug will expand blockchain capabilities to gamifyWeb3 education using Roblox. For those who are not aware about Metahug, here is what it does, Metahug is a global philanthropic organization helping children with limited resources understand and utilize Web3. Metahug will teach children how to use and build Web3 tools via popular youth gaming platform — Roblox, with PraSaga providing free access to its SagaChain to support the initiative.

The MetaHug Back-to-school Roblox Hackathon encourages young students globally to become passionate creators and collaborators on the platform. This initiative delivers education inside the games: to teach various Web 3 topics, including blockchain, DAO, and ownification. The hackathon will last 24 hours on the 10th of September 2022, and winners of the competition will be featured at the next event.

The partnership will be marked with a MetaHug Back-to-school Roblox Hackathon, which encourages young students globally to become creators and collaborators on the platform. This initiative will deliver education inside games: to teach various Web 3 topics, including blockchain, DAO, and ownification. The hackathon will last 24 hours on the 10th of September 2022, and winners of the competition will be featured at the next event.

Providing a Native, Cost-Free Learning Experience for Children Around the World 

Metahug’s vision is to build a compassionate, mindful, and community-driven platform to help children worldwide reimagine their lives by providing free education on utilizing next-generation technology tools. In particular, Metahug is focused on helping children with limited resources, whether that means a lack of physical goods, utilities, or online classes; or limited EQ, defined as those with no emotional support. Through the use of Roblox, PraSaga and Metahug will gamify learning about blockchain and Web3 tools, with the potential to engage millions of children in an environment in which they feel comfortable and familiar.

The Importance of WEB3  

Web3 is the latest iteration of the world wide web. It is set to revolutionize business and society across the globe, so it is critical that those with less access to support and resources are properly educated about its uses. Web3 will be an integral part of Gen Z and Gen Alpha’s lives, providing them with entertainment, educational resources, and employment – including through mechanisms such as NFTs and DAOs.  Metahug’s program provides free courses on how to use Web3, which upskill children in preparation for adulthood, allowing them to direct their education while championing the power of play.

Jay Moore, Chief Collaboration Officer at PraSaga, explains, “The rapid advancement of technology has made it difficult for many to come to grips with the revolutionary power of Web3 and blockchain. But it will change how business, education, and our economy operate. Philanthropy is one of PraSaga’s core tenets. A large part of that is finding ways to ensure future generations across the globe can understand how to use Web3 tools that older generations are still struggling to adopt. Our partnership with Metahug will be invaluable in helping us to achieve this.”

Lian Pham, the co-founder of Metahug, says, “At Metahug, we believe that the future of philanthropy lies in decentralization through Web3 education and mentorship. But proper education on its use and benefits is difficult for many Gen Z and Gen Alphas – those who are set to be most affected by it – to achieve. By joining our DAO, not only do we provide children with hands-on experience with Web3 tools, but we are also providing them with everything necessary to educate, empower, and inspire themselves and others to be leaders of tomorrow and solve humanity’s grand challenges.”

PraSaga is a Swiss Foundation building the next generation of Layer One blockchain. PraSaga’s technology solution solves many of the limitations that plague first-generation Layer One blockchains. The SagaChain successfully addresses lowering transaction fees, extensibility for supply chains, and significantly lowers development costs.

Metahug provides a home to gamify education and coordinate learning, playing, and earning. We work with Play2Learn to develop and promote MetaHug as a secure, transparent, and accountable solution for positive global change. Our community is created to help children with limited resources to reimagine their lives with new child-centric directions. We support their journey by introducing innovative technology such as Web3 education.

The hackathon event features workshops, guest speakers, interviews, fashion shows, concerts, and more — both in the physical and non-physical world. The physical event will be at the Horseshoe Bay Resort, Texas Hill Country, while the non-physical will be on Metahug live stream, YouTube, and Roblox metaverse.

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

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