Month: February 2023

With active learning, training a model moves from being a linear process to a circular one with a strong feedback loop.
Active learning is the future of generative AI: Here’s how to leverage it by Ram Iyer originally published on TechCrunch

During the past six months, we have witnessed some incredible developments in AI. The release of Stable Diffusion forever changed the artworld, and ChatGPT-3 shook up the internet with its ability to write songs, mimic research papers, and provide thorough and seemingly intelligent answers to commonly Googled questions.

These advancements in generative AI offer further evidence that we’re on the precipice of an AI revolution.

However, most of these generative AI models are foundational models: high-capacity, unsupervised learning systems that train on vast amounts of data and take millions of dollars of processing power to do it. Currently, only well-funded institutions with access to a massive amount of GPU power are capable of building these models.

The majority of companies developing the application-layer AI that’s driving the widespread adoption of the technology still rely on supervised learning, using large swaths of labeled training data. Despite the impressive feats of foundation models, we’re still in the early days of the AI revolution and numerous bottlenecks are holding back the proliferation of application-layer AI.

Downstream of the well-known data labeling problem exist additional data bottlenecks that will hinder the development of later-stage AI and its deployment to production environments.

These problems are why, despite the early promise and floods of investment, technologies like self-driving cars have been just one year away since 2014.

These exciting proof-of-concept models perform well on benchmarked datasets in research environments, but they struggle to predict accurately when released in the real world. A major problem is that the technology struggles to meet the higher performance threshold required in high-stakes production environments, and fails to hit important benchmarks for robustness, reliability and maintainability.

For instance, these models often can’t handle outliers and edge cases, so self-driving cars mistake reflections of bicycles for bicycles themselves. They aren’t reliable or robust so a robot barista makes a perfect cappuccino two out of every five times but spills the cup the other three.

As a result, the AI production gap, the gap between “that’s neat” and “that’s useful,” has been much larger and more formidable than ML engineers first anticipated.

Fortunately, as more and more ML engineers have embraced a data-centric approach to AI development, the implementation of active learning strategies have been on the rise. The most sophisticated companies will leverage this technology to leapfrog the AI production gap and build models capable of running in the wild more quickly.

What is active learning?

Active learning makes training a supervised model an iterative process. The model trains on an initial subset of labeled data from a large dataset. Then, it tries to make predictions on the rest of the unlabeled data based on what it has learned. ML engineers evaluate how certain the model is in its predictions and, by using a variety of acquisition functions, can quantify the performance benefit added by annotating one of the unlabeled samples.

By expressing uncertainty in its predictions, the model is deciding for itself what additional data will be most useful for its training. In doing so, it asks annotators to provide more examples of only that specific type of data so that it can train more intensively on that subset during its next round of training. Think of it like quizzing a student to figure out where their knowledge gap is. Once you know what problems they are missing, you can provide them with textbooks, presentations and other materials so that they can target their learning to better understand that particular aspect of the subject.

With active learning, training a model moves from being a linear process to a circular one with a strong feedback loop.

Why sophisticated companies should be ready to leverage active learning

Active learning is fundamental for closing the prototype-production gap and increasing model reliability.

It’s a common mistake to think of AI systems as a static piece of software, but these systems must be constantly learning and evolving. If not, they make the same mistakes repeatedly, or, when they’re released in the wild, they encounter new scenarios, make new mistakes and don’t have an opportunity to learn from them.

Active learning is the future of generative AI: Here’s how to leverage it by Ram Iyer originally published on TechCrunch

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In 2013, Irma Olguin Jr. — a third-generation Mexican American and the first in her family to go to college — was working on making coding instruction available to disadvantaged members of her community. During her work, she met Jake Soberal, an intellectual property lawyer, who shared Olguin’s desire to leverage the tech industry to
Bitwise Industries lands $80M to expand its sprawling software dev business by Kyle Wiggers originally published on TechCrunch

In 2013, Irma Olguin Jr. — a third-generation Mexican American and the first in her family to go to college — was working on making coding instruction available to disadvantaged members of her community. During her work, she met Jake Soberal, an intellectual property lawyer, who shared Olguin’s desire to leverage the tech industry to effect change at the local level.

Olguin and Soberal started by co-founding Geekwise Academy, a coding and tech skills bootcamp that teaches HTML and JavaScript. Then — to boost the hiring pipeline for Geekwise graduates, and under a newly formed parent company called Bitwise Industries — they launched Shift3 Technologies, a software development house that creates managed services for Salesforce, DocuSign and various other software-as-a-service apps.

As Fast Company notes, Bitwise made headlines in 2019 when it raised $27 million — one of the largest Series A rounds ever secured by a company with a female Latinx founder. Historically, female founders have received just 12% of venture capital investment for their businesses.

Olguin and Soberal’s latest venture through Bitwise is commercial real estate — the two develop and turn previously blighted buildings into coworking spaces, restaurants, theaters and more. Evidently impressed with the three-pronged business model, investors — including Kapor Center, Motley Fool, the Growth Equity business within Goldman Sachs Asset Management and Citibank — poured $80 million into Bitwise this week, bringing the company’s total capita raised to over $200 million.

The new cash will be used to support Bitwise’s expansion into Chicago and the growth of the startup’s other locations, Olguin told TechCrunch in an email interview. She and Soberal plan to eventually bring Bitwise’s businesses into 40 cities nationwide.

“Bitwise Industries’ approach has delivered business value in the form of digital transformation nationwide resulting in unprecedented company growth despite global economic instability,” Olguin said. “This latest raise, led by a group of distinguished investors, acknowledges the role technology plays in driving economic impact in previously underserved communities, and validates our model making it possible for us to roll out our proven approach into other parts of the country.”

It’s tough to capture Bitwise’s business in a sentence — it’s sprawling — but the bulk of the company’s revenue comes from partnering with organizations to build digital solutions. Olguin claims that Bitwise has over 500 customers across 15 states, with dozens of agencies and municipalities, for which it’s providing software implementation services.

For example, Bitwise helps Salesforce and DocuSign customers design, build and deploy apps that streamline the business’s process. The firm also delivers custom app and website design services, plus access to a pool of contract-basis software developers.

Bitwise — whose revenue Forbes estimated at $40 million in 2020 — claims to have helped create 15,000 jobs in just one of the cities where it operates. One of its larger initiatives was the “Digital New Deal,” a program in collaboration with California state and local governments to provide students in Bitwise’s apprenticeships the opportunity to work with government agencies on tech-related projects. Olguin says that, to date, Bitwise has renovated over a million square feet of real estate in underserved downtown areas and supported the skilling of over 10,000 people.

“We operate outside major tech hubs to reach people who have been overlooked,” Olguin said. “Bitwise Industries is working to change entrenched bias and make people in positions of power see that sharing privilege actually strengthens companies.”

It’s a noble mission for a commercial venture. To his credit, Olguin doesn’t play down Bitwise’s for-profit status — or its profitability. To the contrary, he spotlights the startup’s many recent strategic acquisitions, which include Salesforce implementation firm Esor Consulting Group, Denver-based software developer Techtonic, DocuSign partner Stria and technical institute the Array School.

“Our funding is driving participation in the digital economy from groups of people who were previously left out of these types of opportunities,” Olguin said. “No one is doing what we are doing at the scale we are doing it across the country.”

Hillel Moerman, a partner at Goldman Sachs, added in an emailed statement: “Goldman Sachs believes that Bitwise has shown a strong track record in training and upskilling talent to successfully find placement in the highly competitive technology labor market. Filling the talent gaps is not only pertinent for the sector, but also key to creating economic opportunity for those in underserved communities and we are excited to support Bitwise in its continued growth.”

Bitwise currently has 10 locations with more than 500 employees, a number that’s triple what it was three years ago. Olguin says that she expects to see similar growth by the year’s end.

Bitwise Industries lands $80M to expand its sprawling software dev business by Kyle Wiggers originally published on TechCrunch

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