Daily Archives: May 9, 2022

When tasked with creating the art for the fourth annual Forbes AI 50 list, it immediately struck me that we should use Artificial Intelligence to generate our deliverable. AI is making considerable strides not only in commercial applications, but in visual art as well. Its artistic endeavors are being auctioned at Sothebys, generating NFT collections, and expediting traditional production processes across media. What does this mean for the future of art, and its reception by the general public?

In most cases for artificially intelligent visual work, such as the main art for AI 50, a General Adversarial Network, or GAN, is trained on a large dataset and compares the individual relationships between each instance of data to understand what belongs and what doesnt. The GAN slowly learns to filter out noise in that data, revealing the similarities, and finally achieving the ability to recreate the material received or to determine whether or not a new input matches it. Its the same machine learning process that goes into loan approval automation or public health diagnostics, the main differences being the end goal and the dataset used. Once trained, the GANs knowledge is contained in a matrix of vectors, referred to as a latent space. The art you are viewing is a composite of 4 separate latent spacewalks, trained using two ready-made datasets provided by Runway ML, and two that were hand-curated by Forbes staff. We walked through the matrix, each step resulting in a frame of a video.

I find this process enjoyable because it renders any single output of the GAN less interesting than a sequence of them. My goal is to engage audiences with art in a dynamic format that is reliant on time and systemic relationships beyond the static perfection of a framed painting or statue. Rather than objects made to create an aesthetic experience and hold value, I like to think of art as a momentary output of artists practices, which are living, breathing, often focused not just on form but on investigation of the world. Its more than giving the viewer a feel-something moment, though thats great. Its also meant to spur thought, influence opinion, and ultimately affect change. This activity is dynamic, purposely fuzzy, ill-defined, loose and inviting serendipitous meanderings and forking pursuits. It generates turbulence and optimistically criticizes, inviting you to do the same.

In the turbulence of this dynamism, theres something else to realize: the so-called author of a work of AI art is no longer a lone creator. The AI, plus the sources of its training material, is her (often unpredictable) partner. Designers and artists have long discussed the idea of programmatic co-creation, see Sol Lewitts Wall Drawings or the Conditional Design Manifesto. But the impending widespread utility of artificial intelligence is going to bring this spirit of collaboration further into the mainstream. Everyone and no one can be a creator, and thats great! It comes at a moment historically pivotal for other reasons that bear the need for collaborative spirit, such as climate change and the reframing of peaceful globalism in the face of a resurging Cold War. The idea of domination has to go away: it is time to rewild, reunite, and return to traditions of communal and ecological reciprocity that we have lost sight of. Although AI is high-tech, and can certainly be used for evil, it has the potential to reinvigorate organic relationships that are essential to a sustainable future. By no means do I see what weve made as very pretty or refined, but maybe that shouldnt be the point anymore.

Lets also take a moment to explore the idea of refinement in AI as it relates to kitsch and ingenuity. Researchers measure the accuracy of a GAN using a metric called the Frchet Inception Distance, or FID, which basically quantifies the accuracy of the GANs output in relation to the data it was trained with. If you want to make a GAN that generates, say, leaf blowers, the lower the FID, the more realistic the leaf blower. If we tried to do this in art, we would immediately arrive at kitsch; its just a thoughtless reiteration of something preexistent. Thats the difficulty in creating something meaningful with AIif its too accurate it's meaningless, and if its too ambiguous it's meaningless again (and yes, swirling images that make buildings look like dogs, Van Goghs or cheese balls almost always fall into the first extreme of this meaninglessness spectrum).

Click here for full coverage of the 2022 AI 50 list.

Not to mention, its pretty hard to find 5000 images of something, and harder still to curate those images in a way that isn't betrayed by the limitations, and underlying discriminations, of humanitys image-making apparatus. For example, we used a series of images capturing well-designed industrial products for one component of the illustration, and I question if it only results in a kitschy pastiche that reinforces the heuristics of that field, as opposed to investigating the universal structures governing them, and profoundly winding the world into a moment. Its critical that as GANs are adopted by the public we avoid setting off on the wrong path. Look around you, what meaning is embedded in the built environment? How are you interacting with the cumulative expression of society?

Ultimately, we will forever continue fusing disciplines into others and discovering the intersectionality of our existence. Art will become a tool and tools will become art and hopefully, at some point soon, decentralization will shift the responsibility of creation and design away from a technocratic few to the cooperative many. When that happens, it will be more important than ever for us to define whats essential to sustaining our lives. I would like to think they will be filled with individuality, curiosity, accountability and optimism.

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Explaining The Art Behind The Forbes AI 50 Design - Forbes

This years inductees reflect the booming VC interest as well as the growing variability in AI-focused startups making unique uses of existing technologies, others developing their own and many simply enabling other companies to add AI to their business model.

The mad scramble to adopt Artificial Intelligence amid the Covid-19 crisis is officially old news. We interact with AI as seamlessly as we do our smartphones, through voice assistants, customer service, automated tasks, self-checkout, fraud detection, in healthcare decisions and infinitely more invisible applications that affect our daily lives. Investments in AI research and applications are set to hit $500 billion by 2024, according to research firm IDC. And PwC predicts AI will contribute $15.7 trillion to the global economy by 2030. With all that money flowing, it can be hard to figure out what the coming thing is, but certain trends do emerge.

Our fourth annual AI 50 list, produced in partnership with Sequoia Capital, recognizes standouts in privately-held North American companies making the most interesting and effective use of artificial technology. This years list launches with new AI-generated design and and multiple funding round announcements that came about after our esteemed panel of judges laid down their metaphorical pencils. Inductees reflect the booming VC interest as well as the growing variability in AI-focused startups making unique uses of existing technologies, others developing their own and many simply enabling other companies to add AI to their business model.

Click here for full coverage of the 2022 AI 50 list.

Hugging Face makes its AI 50 premier as the low-key developer darling turned $2 billion unicorn. The open-source platform (named for the autological emoji) hosts the closest thing to plug-and-play machine learning models, which are used by developers to build features like search, text moderation, image segmentation powered by machine learning and other tools for their own organizations. Hugging Face is also proving an important linchpin in the major leagues, partnering on projects with Qualcomm and Amazon, among others.

The fourth year of AI 50 also heralds the fourth appearance on the list for three startups that also provide AI architecture to major companies. Its no surprise to see Forbes Under 30 alum Alex Wang and his company Scale AI, back with a $7.2 billion valuation and a fresh deal with the Department of Defenses Joint Artificial Intelligence Center. Automated support platform Moveworks marks its AI 50 streak with a $2.1 billion valuation and a breakthrough with conversational AI now able to understand nuance in six languages. And Domino Data Lab returns with an $800 million valuation.

These AI 50 hat tricks the companies whove been on the list three years in a row illustrate the breadth and depth of artificial intelligence and include Abnormal Security (cybersecurity), AMP Robotics (recycling robots), ASAPP (customer service), Cresta (sales support), Databricks (analytics) and Genesis Therapeutics (drug discovery).

But enough about the old dogs the 2022 AI 50 list also features some fasinating new companies. Overjet emerged from stealth in 2021 to become the first-ever dental AI product cleared by FDA. Cofounder and CEO Dr. Wardah Inam, who did her post-doctorate work in biomedical sensing in MITs computer science and artificial intelligence lab, got the idea for Overjet when her new dentist prescribed a treatment plan very different from those she received before. Waabi, founded by AI pioneer and computer scientist Raquel Urtasun, believes its taking a new approach to creating self-driving technology for long-haul trucking. And Aurora Solar cofounders Chris Hopper and Sam Adeyemo were introduced to the inefficiency of solar sales as Stanford Students when they installed panels at a school in East Africa. That frustrating experience inspired them to develop Aurora Solars proprietary measurement and modeling technologies to help speed up and lower the cost of solar power installations.

The Forbes AI 50 list was compiled through a submission process open to any company based in North America, privately held and developing technology that enables machines to learn from experience or new data or perform human-like tasks such as recognizing speech or images, classifying information and predicting outcomes. The application asked companies to provide details on their technology, business model, customers and financials like funding, valuation and revenue history (companies had the option to submit information confidentially, to encourage greater transparency). In total, Forbes received more than 400 entries. From there, our VC partners applied an algorithm to identify more than 120 with the highest quantitative scores and then a panel of 12 expert AI judges in academia, new IPO executives, venture capital and international technology companies identified the 50 most compelling companies.

Private AI companies that were incubated at, largely funded or acquired by large tech, manufacturing or industrial firmsincluding some of the leading autonomous vehicle developerswere not eligible for consideration.

Forbes

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AI 50 2022: North America's Top AI Companies Shaping The Future - Forbes

Arun Subramaniyan, Vice President, Cloud & AI, Strategy & Execution, Intel Corporation

Arun Subramaniyan joined Intel to lead the Cloud & AI Strategy team. Arun joined Intel from AWS, where he led the global solutions team for Machine Learning, Quantum Computing, High Performance Computing (HPC), Autonomous Vehicles, and Autonomous Computing at AWS. His team was responsible for developing solutions across all areas of HPC, quantum computing, and large-scale machine learning applications, spanning $1.5B+ portfolio. Arun founded and grew the global teams for Autonomous Computing and Quantum Computing Go-to-market and solutions at AWS and grew the businesses 2-3x.

Aruns primary areas of research focus are Bayesian methods, global optimization, probabilistic deep learning for large scale applications, and distributed computing. He enjoys working at the intersection of massively parallel computing and modeling large-scale systems. Before AWS, Arun founded and led the AI products team at GEs Oil & Gas division and grew the digital products business successfully. He and his team developed deep learning-augmented hybrid analytics for all segments of the oil & gas industry. Arun led the development of the Digital Twin platform for GE at GEs Global Research Center. The platform continues to enable several thousand engineers to build advanced models efficiently. The asset specific cumulative damage modeling techniques he and his team pioneered define the standard for industrial damage modeling.

As a Six Sigma Master Black Belt, he developed advanced techniques and tools for efficiently modeling large scale systems like jet engine fleets, gas turbines in powerplants, and accelerated design times by 3-4X. Arun is a prolific researcher with a Ph.D. in Aerospace Engineering from Purdue University with 19 granted patents (54 filed), and 50+ international publications that have been cited more than 1000 times with an h-index of 13. He is also a recipient of the Hull Award from GE, which honors technologists for their outstanding technical impact.

Elizabeth Bramson-Boudreau is the CEO and publisher of MIT Technology Review, the Massachusetts Institute of Technologys independent media company.

Since Elizabeth took the helm of MIT Technology Review in mid-2017, the business has undergone a massive transformation from its previous position as a respected but niche print magazine to a widely read, multi-platform media brand with a global audience and a sustainable business. Under her leadership, MIT Technology Review has been lauded for its editorial authority, its best-in-class events, and its novel use of independent, original research to support both advertisers and readers.

Elizabeth has a 20-year background in building and running teams at world-leading media companies. She maintains a keen focus on new ways to commercialize media content to appeal to discerning, demanding consumers as well as B2B audiences.

Prior to joining MIT Technology Review, Elizabeth held a senior executive role at The Economist Group, where her leadership stretched across business lines and included mergers and acquisitions; editorial and product creation and modernization; sales; marketing; and events. Earlier in her career, she worked as a consultant advising technology firms on market entry and international expansion.

Elizabeth holds an executive MBA from the London Business School, an MSc from the London School of Economics, and a bachelors degree from Swarthmore College.

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Powering the next generation of AI - MIT Technology Review

Artificial Intelligence (AI) as an idea is more than 50 years old, though only in the last five years or so has AI come to dominate the world of business.Business communication, that is. Just as it is hard to overestimate the potential of AI to create change at massive scale, it is nearly impossible to exaggerate the degree to which talk of AI in both the news and in the marketing slicks created by PR agencies is hyperbolic and often disconnected from ground realities.

So what about AI in real estate?Over the past several years, many companies have made claims about using AI to disrupt the space; I am myself affiliated with more than one such company.So what, indeed, is the verdict:Reality or rhetoric?

Ill use my authorial perch to play both judge and jury here, but, spoiler alert, the jury is hopelessly hung. As judge, Ill say that so far weve seen far more rhetoric than reality.

To be fair to the AI community, though, lets at the outset state areas in which AI has helped in fundamental ways:

With regard to AVMs and valuations in general, the ability to contextually analyze hundreds of millions of data points and associate them to particular clusters of variables is fairly new and certainly has come about because of advances in AI.Valuations are the core of real estate because so much emanates from the simple question, What is this house worth?While AVMs and the entire valuation industry took a hit with the rhetoric emanating from Zillow as they crashed and burned with Zillow Offers, this was the unfortunate byproduct of a bad business decision and as such cast unfair doubt on AI.

Computer vision allows us to enter the heretofore closed-off portal: The inside of the house. With device proliferation and fantastic upgrades in the power of edge computing and AI on the edge, true condition-adjusted valuations and service-offerings can be considered legitimate for the first time.

Finally, AI has helped make certainelementsof the transaction process better in an incremental and point solution fashion.

Still, the promise of AI remains in the domain of potential.For the most part, real estate is very much a human-led and still error-prone ecosystem.Buyers and sellers do not meet in an open market characterized by full transparency and instantaneity.Cumbersome processes and vested interests continue to play an out-sized role in residential real estate.

And with all the hubbub about democratization asine qua nonof the AI story homeownership has over the last five years become an impossible dream for about one-third of Americans.On these accounts, AI has failed to live up to the hype.

We all need to do more tire-kicking. We need to understand the exact offerings and their precise business value before rejoicing about the advent of AI.The cycle of hyperbole must end and we have to demand that those who say they are game-changers better really change the game. So far, some have, but most have not.

As such, for now, the game remains largely the same.

In other recent proptech news, Aprils PropTech Retrospect highlighted ESG initiatives and appraisal reform in the industry. Ownwell also raised a $5.75 seed round for its property tax services.

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The promise of AI in real estate: whats the verdict? - FinLedger

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Register today!

There is no disputing the privacy trend.It is here.It is unstoppable. And it is one of the few issues in American life that crosses party lines.

Data shows that 86% of people care about privacy for themselves and others with 79% willing to act on it by spending time and money to protect their data. And to those cynics who say people moan about privacy and do nothing, the same study found that 47% have taken action because of a companys data policies.

What does this mean for the trillions of dollars that flow through the U.S. economy as a result of the very same privacy violations that are enraging consumers? It appears to be a tectonic conundrum; consider that Meta conceded that Apples change in its privacy rules has and will cost them billions.

But for companies suffering from the effects of Apples shift to opt-in from opt-out, artificial intelligence could be a solution.

Protecting privacy while allowing the economy to flourish is a data challenge. AI, machine learning, and neural networks have already transformed our lives, from robots to self-driving cars to drug development to a generation of smart assistants that will never double book you.

There is no doubt that AI can power solutions and platforms that protect privacy while giving people the digital experiences they want and allowing businesses to profit.

What are those experiences?Its simple and intuitive to every Internet user. We want to be recognized only when it makes our lives easier.Thatmeans recognizing me so I dont have to go through the painful process of re-entering my data.It means giving me information and yes, serving me an ad that is timely, relevant, and aligns with my needs.

The opportunities within the personalization economy, as I call it, are vast. McKinsey published two white papers about the size of the opportunity and how to do it right. Interestingly and tellingly the word privacy isnt mentioned a single time in either of those white papers.That oversight is remarkable and overlooks the tension between privacy and personalization. If you accomplish the former but sacrifice the latter, millions of consumers will miss out.

AI can find that balance a privacy-respectful personalization, or PRP, and can satisfy our hunger for personalization and recognition, which is hard-wired into the human brain.

Here arefiveways AI can achieve a new era of PRP to enable relevance and support entrepreneurs and brands at the same time.

Importantly, this must be done in a way that requires no coding or friction and can easily be deployed on the open web.

In this way, AI will make sure that the unstoppable privacy trend doesnt disenfranchise those who need to find an audience for their product or service.

As part of this AI revolution, there is an opportunity to create a privacy seal one that goes beyond the must-have limits of compliance with GDRP and other requirements (like TrustArc) and lets the 79% of consumers who will drop their engagement with private-invading brands know that an ad unit is free of cookies.

We know that people will share data if there is true reciprocity in that relationship.For example, an EV manufacturer can use a PRP platform to advertise its cars and bring people into the marketing funnel, but the ultimate goal is to find the most qualified and interested EV buyers at the top of the funnel.

A sophisticated and trained AI model can convert leads that are generated through PRP advertising first-party data by processing the right signals daypart, geography, time spent on ad, for example as well as asking the right questions in a quiz to discern intent.

And rather than have copywriters scribbling away to create thousands of testable messages, marketers can use GPT-3, an AI-led deep-learning model that produces human-like text, to inspire the reciprocity.

The same AI that can build first-party databases can and must be leveraged to connect with users in a more targeted manner.

However, this is not currently taking place. Database marketers still send full-file emails when the ability exists to use AI to truly recognize me as an individual. If the industry doesnt use AI to create a new era of PRP, then marketers will continue to underleverage the databases they have spent billions to build.

In other words, build an AI-driven flywheel that drives revenue in a privacy-first world.

This is an exploding category that is transforming marketing and that, in part, enabled TikTok to generate more traffic than Facebook last year. Recently, Triller bought Julius, combining an influencer platform with software tools in an AI-led combination that will sharpen the ability of brands to find relevant influencers.This is a way to generate even more first-party data, all within the walls of PRP.

One of the most exciting applications of AI is in the field ofadversariallearning. Consumers are looking at privacy holistically not just what marketers do by selling their data and tracking them, but also their efforts in securing their data from breaches.

Human nature has never changed, nor will it. We want to be recognized as unique individuals, and we have highly-tuned instincts when it comes to protecting ourselves and our families from unwelcome and uninvited intrusions. AI can make sure both of those needs are met, and marketers who get to PRP first will lead their businesses into the future.

Doron Gerstel is CEO of Perion.

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5 ways AI can help solve the privacy dilemma - VentureBeat