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AI Must Play a Role in Making Our Stores Safer – ReadWrite

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Shomron Jacob


Increasing incidents of violence and aggression in retail spaces and shopping areas demand no shortage of public policy and societal changes. But I believe an AI-fueled technology shift will need to be part of the response. Technology isn’t a panacea to all that is going on, of course. It’s far from it. But technology – and AI specifically – might be the fastest and most realistic action to take that can still alter the trajectory of store violence and give businesses an opportunity to be proactive. And it can have an impact.

The precautions and processes to better ensure the safety of shoppers has never been more important. The issue now has the ear of retail CEOs and its board members. Action needs to follow. In addition to the moral imperative to protect human lives however possible, shopper safety is also a customer experience and reputational issue. Stores considered unsafe, especially if they’re the site of incidents involving weapons, theft, or aggression, will see reduced customer traffic. As with brands’ conscious social responsibility, safety is a top-level issue that speaks to retailers’ customer relationships and core values.

The good news is that startups and technology companies have begun more rapidly building AI-driven applications with unprecedented capabilities for remediating a range of human-threat scenarios. We’re seeing more every month, and there is truly remarkable technology emerging. The promise – and the power – of AI here is to detect and react to threats before it’s too late. Teaming store and parking lot cameras with AI-trained applications to recognize weapons, aggression, and suspicious behaviors gives stores proactive power. And it can save lives.

The importance of physical safety for retailers

Camera-centric security strategies have long been budgeted under loss prevention – but this isn’t that. Rather, the stakes of personal safety demand that emerging AI capabilities play a larger (and public-facing) role in brand conversations. AI has until now been mostly transparent to customers in its ‘traditional’ retail use cases. Stores use AI for handling product recommendations, inventory, predictions, customer counting, etc. But AI-based security can take the spotlight as a feature that provides confidence and assurance to shoppers. Doing so increases both brand trust and store traffic. Strategies in this new and distinct category – call it “threat prevention” – offer stores the opportunity to demonstrate and differentiate themselves as the safest locations for customers to do their shopping – because they are.

The pandemic provides a clear example of the effects that meaningful and visible safety precautions can have on boosting customer traffic. Businesses were able to offer safer customer experiences by providing hand sanitizer, systematically wiping down carts, and installing protective plexiglass. As a result, customers felt the trust and comfort they needed to conduct their business. AI-based shopper safety protections can achieve that same impact on customer perceptions.

An opportunity for real differentiation

This potential for differentiation is analogous to recent shifts in the digital security world. Traditionally, IT security and data privacy have been necessary business expenses with no particularly discernable brand benefit. Companies like Apple are changing this: Apple commercials now promote devices as the best-in-class for keeping personal data private. For example, the fact that iOS automatically strips geolocation data from shared photos is no longer just a safety feature – it’s also a means of winning over customers.

In much the same way, AI will allow businesses to differentiate through safety. I expect we will soon see stores leverage public-facing demonstrations of AI capabilities, coupled with branding that establishes their stores as “the safest place to shop.” Delivering that comfort and security will help earn shoppers’ attention and consideration.

AI approaches to physical threat detection and prevention

The startup space is seeing a surge in new companies specializing in AI threat detection and prevention. This emerging technology area is also seeing rapid growth and acquisitions, as VCs recognize its tremendous potential.

AI-powered retail threat prevention combines security cameras, image recognition, and advanced machine learning algorithms. Success here means identifying weapons, full face-covering masks, aggressive behavior, and other tell-tale indicators of developing threats. This capacity for AI to automatically detect these dangers has already arrived. This is not an AI research challenge. This is now a matter of applying existing AI training models to identify potential threat scenarios that retail spaces can incur. Leveraging security footage, developers are working on AI object detection training for each possible threat context, from weapons themselves to weapons held or worn by subjects, subjects with multiple weapons, subjects within busy crowds, and many more scenarios. The result is AI software with algorithms able to perform accurate and instantaneous threat detection, followed by decisive automated responses.

Take an example where individuals in a store parking lot exit a vehicle carrying concealed guns and knives. Footage from parking lot security cameras is processed by AI software that recognizes these weapons, seeing distinctive signs that a human security officer looking at the feed would not. The AI system activates security protocols. The store locks the door, calls the police, and implements immediate precautions to keep shoppers safe. In this way, AI enables retailers to prevent incidents before they happen.

Better systems, better reputations

Importantly, AI threat prevention is fully capable of enhancing shopper safety for retail brands using the existing security cameras and equipment they’ve already invested in. (Though that said, as retailers naturally upgrade their equipment, adding wireless and IP-based cameras will enable AI applications on top of those cameras to become even more seamless.) Also expect to see AI that take advantage of thermal imaging and other specific capabilities to enter the market for retailers as well, as the threat prevention technology landscape broadens and matures.

Retailers are well-versed in the dynamics of customers avoiding stores with bad reputations. They also understand the draw and comfort of locations with more prestige and trust. AI-based shopper safety is now at a very early stage, but as startups and other companies introduce nascent technology, retailer adoption is going to unfold quickly. By their nature, AI applications in this space should be able to scale rapidly. And demand will likely require that, as retailers compete to offer reassurance and provable safety measures for their shoppers.

Shomron Jacob

Engineering Manager, Applied Machine Learning and AI

Shomron Jacob is an Engineering Manager focused on Applied Machine Learning and AI at Iterate.ai, creator of the Interplay low-code platform for rapidly prototyping AI-based applications across industries. Shomron began his career as a software engineer but soon found himself learning ML/AI, and switched his professional direction to follow it. He lives in Silicon Valley.

Politics

Fintech Kennek raises $12.5M seed round to digitize lending

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London-based fintech startup Kennek has raised $12.5 million in seed funding to expand its lending operating system.

According to an Oct. 10 tech.eu report, the round was led by HV Capital and included participation from Dutch Founders Fund, AlbionVC, FFVC, Plug & Play Ventures, and Syndicate One. Kennek offers software-as-a-service tools to help non-bank lenders streamline their operations using open banking, open finance, and payments.

The platform aims to automate time-consuming manual tasks and consolidate fragmented data to simplify lending. Xavier De Pauw, founder of Kennek said:

“Until kennek, lenders had to devote countless hours to menial operational tasks and deal with jumbled and hard-coded data – which makes every other part of lending a headache. As former lenders ourselves, we lived and breathed these frustrations, and built kennek to make them a thing of the past.”

The company said the latest funding round was oversubscribed and closed quickly despite the challenging fundraising environment. The new capital will be used to expand Kennek’s engineering team and strengthen its market position in the UK while exploring expansion into other European markets. Barbod Namini, Partner at lead investor HV Capital, commented on the investment:

“Kennek has developed an ambitious and genuinely unique proposition which we think can be the foundation of the entire alternative lending space. […] It is a complicated market and a solution that brings together all information and stakeholders onto a single platform is highly compelling for both lenders & the ecosystem as a whole.”

The fintech lending space has grown rapidly in recent years, but many lenders still rely on legacy systems and manual processes that limit efficiency and scalability. Kennek aims to leverage open banking and data integration to provide lenders with a more streamlined, automated lending experience.

The seed funding will allow the London-based startup to continue developing its platform and expanding its team to meet demand from non-bank lenders looking to digitize operations. Kennek’s focus on the UK and Europe also comes amid rising adoption of open banking and open finance in the regions.

Featured Image Credit: Photo from Kennek.io; Thank you!

Radek Zielinski

Radek Zielinski is an experienced technology and financial journalist with a passion for cybersecurity and futurology.

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Politics

Fortune 500’s race for generative AI breakthroughs

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Deanna Ritchie


As excitement around generative AI grows, Fortune 500 companies, including Goldman Sachs, are carefully examining the possible applications of this technology. A recent survey of U.S. executives indicated that 60% believe generative AI will substantially impact their businesses in the long term. However, they anticipate a one to two-year timeframe before implementing their initial solutions. This optimism stems from the potential of generative AI to revolutionize various aspects of businesses, from enhancing customer experiences to optimizing internal processes. In the short term, companies will likely focus on pilot projects and experimentation, gradually integrating generative AI into their operations as they witness its positive influence on efficiency and profitability.

Goldman Sachs’ Cautious Approach to Implementing Generative AI

In a recent interview, Goldman Sachs CIO Marco Argenti revealed that the firm has not yet implemented any generative AI use cases. Instead, the company focuses on experimentation and setting high standards before adopting the technology. Argenti recognized the desire for outcomes in areas like developer and operational efficiency but emphasized ensuring precision before putting experimental AI use cases into production.

According to Argenti, striking the right balance between driving innovation and maintaining accuracy is crucial for successfully integrating generative AI within the firm. Goldman Sachs intends to continue exploring this emerging technology’s potential benefits and applications while diligently assessing risks to ensure it meets the company’s stringent quality standards.

One possible application for Goldman Sachs is in software development, where the company has observed a 20-40% productivity increase during its trials. The goal is for 1,000 developers to utilize generative AI tools by year’s end. However, Argenti emphasized that a well-defined expectation of return on investment is necessary before fully integrating generative AI into production.

To achieve this, the company plans to implement a systematic and strategic approach to adopting generative AI, ensuring that it complements and enhances the skills of its developers. Additionally, Goldman Sachs intends to evaluate the long-term impact of generative AI on their software development processes and the overall quality of the applications being developed.

Goldman Sachs’ approach to AI implementation goes beyond merely executing models. The firm has created a platform encompassing technical, legal, and compliance assessments to filter out improper content and keep track of all interactions. This comprehensive system ensures seamless integration of artificial intelligence in operations while adhering to regulatory standards and maintaining client confidentiality. Moreover, the platform continuously improves and adapts its algorithms, allowing Goldman Sachs to stay at the forefront of technology and offer its clients the most efficient and secure services.

Featured Image Credit: Photo by Google DeepMind; Pexels; Thank you!

Deanna Ritchie

Managing Editor at ReadWrite

Deanna is the Managing Editor at ReadWrite. Previously she worked as the Editor in Chief for Startup Grind and has over 20+ years of experience in content management and content development.

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UK seizes web3 opportunity simplifying crypto regulations

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Deanna Ritchie


As Web3 companies increasingly consider leaving the United States due to regulatory ambiguity, the United Kingdom must simplify its cryptocurrency regulations to attract these businesses. The conservative think tank Policy Exchange recently released a report detailing ten suggestions for improving Web3 regulation in the country. Among the recommendations are reducing liability for token holders in decentralized autonomous organizations (DAOs) and encouraging the Financial Conduct Authority (FCA) to adopt alternative Know Your Customer (KYC) methodologies, such as digital identities and blockchain analytics tools. These suggestions aim to position the UK as a hub for Web3 innovation and attract blockchain-based businesses looking for a more conducive regulatory environment.

Streamlining Cryptocurrency Regulations for Innovation

To make it easier for emerging Web3 companies to navigate existing legal frameworks and contribute to the UK’s digital economy growth, the government must streamline cryptocurrency regulations and adopt forward-looking approaches. By making the regulatory landscape clear and straightforward, the UK can create an environment that fosters innovation, growth, and competitiveness in the global fintech industry.

The Policy Exchange report also recommends not weakening self-hosted wallets or treating proof-of-stake (PoS) services as financial services. This approach aims to protect the fundamental principles of decentralization and user autonomy while strongly emphasizing security and regulatory compliance. By doing so, the UK can nurture an environment that encourages innovation and the continued growth of blockchain technology.

Despite recent strict measures by UK authorities, such as His Majesty’s Treasury and the FCA, toward the digital assets sector, the proposed changes in the Policy Exchange report strive to make the UK a more attractive location for Web3 enterprises. By adopting these suggestions, the UK can demonstrate its commitment to fostering innovation in the rapidly evolving blockchain and cryptocurrency industries while ensuring a robust and transparent regulatory environment.

The ongoing uncertainty surrounding cryptocurrency regulations in various countries has prompted Web3 companies to explore alternative jurisdictions with more precise legal frameworks. As the United States grapples with regulatory ambiguity, the United Kingdom can position itself as a hub for Web3 innovation by simplifying and streamlining its cryptocurrency regulations.

Featured Image Credit: Photo by Jonathan Borba; Pexels; Thank you!

Deanna Ritchie

Managing Editor at ReadWrite

Deanna is the Managing Editor at ReadWrite. Previously she worked as the Editor in Chief for Startup Grind and has over 20+ years of experience in content management and content development.

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