Founded in 2012, Qloo has been a journey to understand culture and taste on a global basis, and empower software developers and enterprises to decode and predict consumer taste in a high ethical and trusted way. Venture backed, the company has raised over $30M across Seed, Series A, and Series B fundraising rounds. Investors include: Eldridge, AXA Venture Partners, Chaifetz Group, Jaws Ventures, MDC Ventures, and a range of individuals across the entertainment and other strategic investors, including Leonardo DiCaprio, Elton John and Starwood Hotels founder, Barry Sternlicht.
In interviewing Alex Elias, CEO and Founder, last week, his passion for making our world a better place had me almost dancing around my office after the interview. So incredibly bright, authentic and kind, he acutely understands that major brands and consumers must look beyond trends and uncover the connections behind people’s taste in the world around them.
Humans are complex and with the vast amount of data that can be sourced from B2C companies in each domain, Qloo brings together over 750+ billion cultural correlations and insights to its customers. Weekly more data and parameters are being found that challenge what we think we know about culture and taste. We had an interesting discussion on correlating health risks globally as a major factor impacting taste and how important this is, as well as emotional (voice) intelligence sourced from YouTube videos to evaluate global sentiment shifts (partnering with Google opens up opportunities to look at more macro perspectives and insurance providers).
At the end of the day, humans cannot survive without food so ensuring we bring to market what the culture and taste preferences are is big business.
How big is big?
The Food and Land Use Coalition estimated the total value of the global food system to be USD 10 trillion in 2019. The food industry is worth about $1.5 trillion annually in the United States.
We are also more than what we enjoy culturally and what we taste, and they are on a mission to construct one of the most intelligent AI models known to mankind, at the same time they are faced with major hurdles of ensuring ethical practices in AI, particularly in the wake of sweeping privacy and AI regulations and private sector moves such as Google’s decision to phase out tracking cookies. At Qloo, the company is striving to provide a solution that not only aligns with ethical standards, but also empowers companies to understand its customers, without compromising privacy.
With major customers relying on the Qloo insights, from major brands like: Ticketmaster, Netflix, Michelin, JCDecaux, Samsung, PepsiCo, Starbucks, Buzzfeed, FC Barcelona, and the NY Mets to name a few, Qloo is making a difference.
As an example of the impact Qloo is having, let’s look at Michelin’s guides, one of their customers, which are reliant on relating to people and their interests. The AI model that Qloo has developed allows them to predict taste for any target audience and map relationships within and between cultural domains, including: music, film, TV, dining, nightlife, fashion, books, travel and tech. The Chief Product Office at Michelin Experiences, Michael Davis, shared: “The golden goose in travel is how we can give you access to discovery and access to scale but also make it personalized for your particular needs. In New York, we might have two hotels on the same block, but they’re very different in their style. If you went to Google, for example, you would get the same sort of recommended things to do because they are both located within a couple of hundred feet of one another. With Qloo it allows Michelin to take into account the customer profile or the hotel’s profile, its style, its atmosphere, its design, and create a guide that’s more tuned for that type of guest than the one that’s staying down the block. So you might get museums recommended, but one might be the Museum of Sex in New York versus the Whitney.”
Pretty slick guided intelligence.
In speaking to Alex, I was curious as to who they were watching in this exciting field of personalization and geospatial insights, he acknowledged: Dynamic Yield, Foursquare, and the Google Maps API as noteworthy competitors. Dynamic Yield is adept at leveraging companies’ existing data for personalization, while Foursquare thrives on its own dataset derived from user check-ins, and the Google Maps API offers data on individual businesses, but lacks insights into a company’s customer base.
However, what sets Qloo apart is its unique ability to offer a comprehensive and multidimensional approach, so that its customers achieve significant personalization, with minimal supplied context and its technology is often complementary to seeming competitors who operate within a more generalized recommendation engine, hence Qloo brings unique turn-key knowledge.
Unlike competitors, Qloo’s API goes beyond mere personalization or location-based insights. With a profound understanding of consumer behavior of over 575 million entities worldwide, its technology enables contextualized personalization and deep insights into the intricate connections behind people’s tastes. From music to film and beyond, its specialized data, covers the entire planet, allows for precision down to hundreds of feet. It also provides 750+ billion cultural correlations and insights, processing millions of new cultural data points daily to keep its clients at the forefront of evolving tastes, deploying up to 200 new meta-models each week.
There is no question in my mind that Qloo’s commitment to a comprehensive and dynamic approach sets it apart as a leader in the field, offering unparalleled personalization and insights for our clients.
AI Ethical Practices
For those who follow me you already know how concerned I am about AI ethical practices and ensuring company’s we profile in Forbes have a strong commitment to legal compliance to ensure their AI systems are fair, responsible, and free from copyright infringement.
With the recent developments of generative AI market leaders under the microscope like OpenAI where Mark Benioff recently accused Sam Altman of “stealing copyrighted content to train its AI models,” has ethicists cringing and wondering where are our technology leaders values are as the world-wide internet is not their source code.
Unlike many technology leaders, Qloo has focused on branding itself as a leader in providing AI-driven insights and personalization, while maintaining the highest ethical standards from the get go.
Such a refreshing and role model story to share with all our readers.
Digging into how Qloo is approaching this, Alex shared their five Ethical AI methods.
1. Proprietary Named Entity Graph: Our AI operates on a self-developed named entity graph, using Qloo’s unique embeddings. This proprietary approach eliminates reliance on external data sources, ensuring all data classifications are unbiased and ethically sound, there is no external copyrighted material that is involved in the training pipeline (on the way in or out).
2. Mathematical Co-Occurrences for Training: The foundation of Qloo’sAI’s learning process is based on the mathematical co-occurrences of named Qloo entities. This method is purely analytical, avoiding any potential copyright issues, and ensures that Qloo’s insights and recommendations are derived from our own ethical data practices.
3. Rigorous Legal Compliance: Qloo engages external legal counsel to review all its data practices, ensuring they align with the highest standards of intellectual property laws and data privacy regulations. This guarantees that all of their AI development processes are legally sound and ethically responsible.
4. Algorithmic Transparency and Fairness: Their AI algorithms are transparent and regularly audited. This ensures the fairness and integrity of Qloo’s data classifications, maintaining the trust of its clients and partners.
5. Continuous Improvement in AI Ethics: Qloo is committed to continually advancing its AI capabilities, staying abreast of evolving standards in AI ethics and legal requirements. This proactive stance ensures Qloo’s AI remains not only innovative but also is ethically and legally compliant.
Alex went on to explain the rigor of how far Qloo has taken ethical AI ensuring all of its employees, suppliers and its customers are made aware of their commitment to ethical AI, and they have developed a clearly defined ethical policy that prioritizes transparent, and responsible AI development and deployment. It emphasizes fairness and non-discrimination in AI systems, upholds data privacy and security, and ensures transparency and explainability in AI decisions. The company is committed to continuous monitoring and improvement of our AI applications, maintaining accountability, and engaging with stakeholders to address their concerns. Regular employee training on AI ethics, compliance with laws and regulations, and a focus on using AI for social good are integral to Qloo’s approach. The policy also includes protections for whistleblowers, fostering an environment where ethical concerns can be raised without fear of retribution. This policy reinforces Qloo’s commitment to aligning AI technology development with societal values and ethical principles.
In addition, central to their methodology is the use of advanced metrics and algorithms specifically designed to detect and mitigate biases within datasets and models. This approach allows Qloo to assess the fairness of our AI systems with a keen focus on sensitive attributes such as age, gender, and race, enabling the company to fine-tune its AI models to align with their ethical standards.
They also use visualization tools to probe the behavior of their AI models which is crucial for conducting in-depth counterfactual analyses and for comparing the performances of the AI models across diverse demographic segments.
Additionally, their approach to AI ethics is deeply embedded throughout their technology development lifecycle, as they rigorously follow a checklist that ensures considerations like: data privacy, fairness, and model robustness are integrated at every stage of their AI projects. This holistic approach not only aligns with their high ethical standards, but also promotes a culture of responsibility and awareness within their employee base.
Having reviewed their approaches and methods, there is no question in my mind that Qloo is at the forefront of ethical AI development, consistently aligning their work with the broader societal values and expectations. The Qloo detailed ethical AI policy you can see at the references section at the end this article.
CEO Entrepreneurial Lessons Learned
One of the most important passions I have in writing these articles is ensuring I listen to the voice of the CEO in describing their most important lessons learned in what it takes to build a great company that has sustaining foundations.
This is what Alex had to say (verbatim).
“In my entrepreneurial journey with Qloo, I’ve come to realize the immense value of choosing ethical practices for the long term, even if it means forgoing immediate rewards. When we introduced our privacy-centric API, the market was rife with skepticism. We were in an era dominated by companies like Acxiom, who offered direct identity-based data connections, and the digital landscape was awash with web-based cookies operating under minimal consent protocols. In this environment, where identity-based targeting was king, many wondered why there was a need for a tool like ours – one that prioritized ethical considerations and relied on probabilistic methods. Our approach seemed unconventional, particularly when set against the backdrop of existing methods that were less ethical but widely accepted and seemingly compliant with regulations. We faced a significant challenge: convincing the market of our value. We pivoted our strategy, focusing on our unique strengths. One such strength was our ability to derive meaningful insights even in the absence of direct identity-based data. For example, Qloo could generate a prediction about the classical literary and music preferences of a shopper likely to frequent a particular store in Paris’s 7th arrondissement – a task seemingly impossible without direct data but made feasible through our innovative approach. This journey has taught me a crucial lesson: In entrepreneurship, reimagining perceived weaknesses as strengths is not just a tactic; it’s essential for survival. Over 12 years, I’ve seen firsthand that maintaining ethical standards can initially slow your momentum. Yet, this approach eventually becomes a formidable competitive edge. The key lies in resilience – judiciously managing your resources to navigate short-term challenges and ensuring your venture’s longevity. This insight is invaluable for fellow entrepreneurs: Upholding ethical integrity may test your endurance, but it ultimately lays the foundation for enduring success and trust. This is a testament to the power of ethical entrepreneurship in an increasingly data-driven world.”
In summary, Qloo not only offers us powerful insights on the unique intricacies of culture, taste to predict trends using AI and machine learning methods, underpinned with a solid foundation on ethical principles, practices and inspection methods – this is one of the few companies I have found that really walks the talk practicing responsible and trusted AI. As the regulatory environment kicks in with the upcoming EU AI Act, US Act and other countries coming on stream over the next three years, Qloo is well positioned ahead of many market players that have not understood how crucial it is to build sound ethical practices – end to end. But what they are also doing is improving the Food Supply Chain Intelligence and making it far more efficient so waste is curtailed. Although we did not speak about this value contribution to their AI science contributions, I left feeling this was one of the most significant value propositions that Qloo is bringing to help support the ravenging of Mother Earth.
References and Additional Research Sources
Interview with CEO Alex Elias- Feb 15th, 2024
Qloo’s AI Ethical Policy
1. Commitment to Ethical AI
Qloo is committed to developing and deploying AI technologies in a manner that is ethical, transparent, and aligned with societal values.
2. Fairness and Non-Discrimination
Qloo will actively work to identify and mitigate biases in datasets and algorithms to prevent discrimination based on race, gender, age, or other protected characteristics.
3. Transparency and Explainability
Qloo’s AI systems will be transparent in their operations. We will strive for explainability in AI decisions, providing clear, understandable reasons for AI behaviors and outputs and a comprehensive range of output metrics that empower clients to make decisions accordingly whether qualitatively or programmatically.
4. Privacy and Data Governance
Qloo upholds the highest standards of data privacy and security and will not leverage any form of personally identifiable information or copyrighted information in any of the modeling pipelines. Data governance policies will be established to ensure ethical data handling and protection.
5. Accountability and Responsibility
Qloo will take full responsibility for the AI systems we develop and deploy. We will ensure that there are mechanisms for accountability in case of any adverse impacts or ethical concerns.
6. Continuous Monitoring and Improvement
Qloo will continuously monitor our AI systems post-deployment for any ethical issues or biases and take prompt action to improve them.
7. Stakeholder Engagement
We will engage with stakeholders, including customers, employees, and the broader community, to understand and address their concerns related to AI ethics.
8. Employee Training and Awareness
We will provide training for our employees on AI ethics to ensure they are aware of and can effectively implement these principles in their work.
9. Compliance with Laws and Regulations
We will comply with all applicable laws, regulations, and industry standards related to AI and data usage.
10. Collaboration and Research
We will collaborate with academic, industry, and regulatory bodies to advance ethical AI practices and contribute to research in this field.
11. Whistleblower Protection
We will protect employees and other stakeholders who raise concerns about ethical issues in our AI systems, ensuring they can report issues without fear of retribution.
This AI Ethics Policy reflects our dedication to responsible AI practices and is reviewed and updated regularly together with outside counsel to adapt to new challenges and developments in the field of AI and data science.
More About the CEO, Alex Elias
Alex Elias, the Founder of Qloo, has a profound and nuanced understanding of the AI ethical landscape, shaped by his extensive background in internet privacy. Prior to founding Qloo, Alex completed his J.D. at NYU Law School, focused on internet privacy, laid the groundwork for his views on AI ethics. During his graduate studies Alex did substantial critical analyses of internet privacy issues, including proposing empirical evaluations of the effectiveness of clickwrap agreements and evaluating the Consumer Privacy Bill of Rights, a precursor to recent privacy regulations like GDPR and CCPA.
Alex firmly believes that the ethical integrity of AI begins with its input data. His stance is clear: if the input data is ethically compromised, whether by copyright infringement or sensitive identity issues, the entire AI process is tainted. He likens it to the principle of “garbage in, garbage out” but in the context of ethical training data.
Under Alex’s leadership, Qloo has been meticulous in assembling its data corpus. The company ensures ethical compliance by securing explicit permissions for primary embeddings and developing its own data pipelines. This commitment extends to the way Qloo handles privacy in its AI models.
In an era where technology companies face growing public distrust, especially regarding AI, Alex advocates for a balance between innovation and responsible practices. He emphasizes the importance of fairness, transparency, and individual privacy. The shift towards privacy-conscious AI in recent years isn’t solely regulation-driven; it’s also a response to increasing consumer demand for privacy.
Qloo, under Alex’s guidance, has been a forerunner in privacy-centric AI, a journey that started well before the enactment of GDPR and the current AI wave by well over half a decade. The company’s approach to predictions is unique: they are pre-anonymized, requiring no personal information (even pseudonymized stable identifiers like Cookies). Instead, Qloo focuses on collecting individual taste points and demographic/geographic data. This method allows for a more contextual approach, embedding tastes directly onto the entities, ensuring personalization that is privacy-centric by design. Qloo’s commitment to privacy and ethical AI practices has been unwavering, long preceding the current commercial focus on these values.
