Remember the Apple car rumors? Project Titan, as it's apparently called, is still progressing, with perhaps, a dose of reality. Bloomberg's Mark Gurman says the company's decade-old project has shifted from creating a fully self-driving car to an EV more like Tesla's. The car's autonomous features have reportedly been downgraded from a Level 5 system (full automation) to a Level 4 system (full automation in some circumstances) -- and now to Level 2 (partial automation). For context, Tesla's Autopilot is Level 2. Level 2 doesn't have a formal description yet.

"At every major inflection point, there has to be an evolution," says Humera Shahid, chief diversity, equity, and inclusion officer and head of talent development at Intuit. "That means the way that we're organized is different, [and] the skill sets that we need are rapidly changing, especially in technology," Shahid says. Intuit has undergone many iterations since its inception in 1983 as a digital checkbook to help people pay bills, later known as Quicken. Over the past four decades, the $13 billion software firm has repeatedly reinvented itself, notably selling Quicken in 2016, then pivoting from operating solely as a tax and accounting platform to a more holistic financial platform for individuals and small businesses. It recently made two big acquisitions in Credit Karma ($8.1 billion) and MailChimp ($12 billion) as part of its data play.

On any given day, Lily AI runs hundreds of machine learning models using computer vision and natural language processing that are customized for its retail and ecommerce clients to make website product recommendations, forecast demand, and plan merchandising. But this spring when the company was in the market for a machine learning operations platform to manage its expanding model roster, it wasn't easy to find a suitable off-the-shelf system that could handle such a large number of models in deployment while also meeting other criteria. Some MLops platforms are not well-suited for maintaining even more than 10 machine learning models when it comes to keeping track of data, navigating their user interfaces, or reporting capabilities, Matthew Nokleby, machine learning manager for Lily AI's product intelligence team, told Protocol earlier this year. "The duct tape starts to show," he said. Nokleby, who has since left the company, said that for a long time Lily AI got by using a homegrown system, but that wasn't cutting it anymore.

When Diane Chang began working at Intuit, maker of Turbo Tax and Quick Books, more than a decade ago in 2009 as a data scientist, there were only a few other people performing that role at the company. Being a data scientist back then reminded her of a time when she had worked for a consulting firm. "We had to convince people that they should work with us, and that they'd want to work with us, and that we could help provide value. There was a lot of selling initially and explaining and describing what we could do," she says. There's more demand than there is supply for data scientists." Chang, now director of data science at the company, provided an inside view into how Intuit is leveraging data science today. The company says it has evolved into an AI-driven platform company. Chang provided insights into the business trends have impacted the company and what data science trends are getting the attention of the C-suite. One of the major business trends that has impacted Intuit's ...

Many of us are familiar with Intuit's (NASDAQ:INTU) industry-leading products in personal taxes (Turbo Tax) and small business accounting (QuickBooks). However, the company has expanded well beyond these two areas and assembled a portfolio of products that have improved and will continue to improve the financial lives of its customers. On Intuit's website, CEO Sasan Goodarzi described their mission statement as follows: We are a purpose-driven, values-driven company. Our mission to power prosperity around the world is why we show up to work every single day to do incredible things for our customers. Our values guide us and define what we stand for as a company.

Time and again, data science has been touted as the hottest career option in the 21st century. But, do you know what goes on in the life of a data scientist? To understand this, Analytics India Magazine got in touch with Sadaf Sayyad, data scientist at Intuit, who walked us through a typical day at her work, alongside sharing interesting instances, career growth, and the impact she is adding to the team and the ecosystem. "For a data scientist, a typical day depends on the phase of the project one is working on. But, on a high level, my day starts with checking emails and messages for any urgent tasks. Then, we have a stand-up meeting to discuss the progress of the project and blockers followed by planning my day," said Sayyad.

AI practitioners are all too familiar with statistics that over 80% of AI projects fail. A lot has been said about what organizations and data science teams can do to increase this low success rate. Nonetheless, even organizations with established machine learning (ML) practices and high-end AI teams struggle. Some AI initiatives become transformational for the business, while others show little return on investment or never even come to fruition. Of course, this isn't unique to AI projects, but since data science is a fairly new discipline, there's another factor impeding success: not everything can be solved with AI.

Machine learning models that first learn a representation of a domain in terms of human-understandable concepts, then use it to make predictions, have been proposed to facilitate interpretation and interaction with models trained on high-dimensional data. However these methods have important limitations: the way they define concepts are not inherently interpretable, and they assume that concept labels either exist for individual instances or can easily be acquired from users. These limitations are particularly acute for high-dimensional tabular features. We propose an approach for learning a set of transparent concept definitions in high-dimensional tabular data that relies on users labeling concept features instead of individual instances. Our method produces concepts that both align with users' intuitive sense of what a concept means, and facilitate prediction of the downstream label by a transparent machine learning model. This ensures that the full model is transparent and intuitive, and as predictive as possible given this constraint. We demonstrate with simulated user feedback on real prediction problems, including one in a clinical domain, that this kind of direct feedback is much more efficient at learning solutions that align with ground truth concept definitions than alternative transparent approaches that rely on labeling instances or other existing interaction mechanisms, while maintaining similar predictive performance.

One of the most frequently-used phrases at business events these days is "the future of work." It's increasingly clear that artificial intelligence and other new technologies will bring substantial changes in work tasks and business processes. But while these changes are predicted for the future, they're already present in many organizations for many different jobs. The situation brings to mind the William Gibson comment, "The future is already here--it's just not evenly distributed." The jobs and work processes described below are an example of this phenomenon.

The Oakland Unified School District this week issued an apology for sending out a survey that included a historically racist term for people of Asian descent. However, a movement is underway to prevent bad word choices. "I think that words do matter, so I think that you do have to be very mindful of the words that you use," says Jaye Bailey, Valley Transportation Authority's head of civil rights and employee relations. Whether it's a transit agency like VTA or a private company, attention to messaging has never been greater as a result of the social justice movement. RELATED: Oakland Unified School District apologizes after'historically racist' term used in survey "You really work hard to normalize the language within your organization so that everybody is aware of it so that it becomes second, second nature," she added.