Personal blog of Yzmir Ramirez

There are few topics as hot right now in the enterprise as customer experience management, that ability to collect detailed data about your customers, then deliver customized experiences based on what you have learned about them. To help understand the challenges companies face building this kind of experience, we are bringing Segment CEO Peter Reinhardt to TechCrunch Sessions: Enterprise on September 5 in San Francisco (p.s. early-bird sales end this Friday, August 9).

At the root of customer experience management is data — tons and tons of data. It may come from the customer journey through a website or app, basic information you know about the customer or the customer’s transaction history. It’s hundreds of signals and collecting that data in order to build the experience where Reinhardt’s company comes in.

Segment wants to provide the infrastructure to collect and understand all of that data. Once you have that in place, you can build data models and then develop applications that make use of the data to drive a better experience.

Reinhardt, and a panel that includes Qualtrics’ Julie Larson-Green and Adobe’s Amit Ahuja, will discuss with TechCrunch editors the difficulties companies face collecting all of that data to build a picture of the customer, then using it to deliver more meaningful experiences for them. See the full agenda here.

Segment was born in the proverbial dorm room at MIT when Reinhardt and his co-founders were students there. They have raised more than $280 million since inception. Customers include Atlassian, Bonobos, Instacart, Levis and Intuit .

Early-bird tickets to see Peter and our lineup of enterprise influencers at TC Sessions: Enterprise are on sale for just $249 when you book here; but hurry, prices go up by $100 after this Friday!

Are you an early-stage startup in the enterprise-tech space? Book a demo table for $2,000 and get in front of TechCrunch editors and future customers/investors. Each demo table comes with four tickets to enjoy the show.

The enterprise software and services-focused accelerator Alchemist has raised $4 million in fresh financing from investors BASF and the Qatar Development Bank, just in time for its latest demo day unveiling 20 new companies.

Qatar and BASF join previous investors, including the venture firms Mayfield, Khosla Ventures, Foundation Capital, DFJ and USVP, and corporate investors like Cisco, Siemens and Juniper Networks.

While the roster of successes from Alchemist’s fund isn’t as lengthy as Y Combinator, the accelerator program has launched the likes of the quantum computing upstart Rigetti, the soft-launch developer tool LaunchDarkly and drone startup Matternet .

Some (personal) highlights of the latest cohort include:

• Bayware: Helmed by a former head of software-defined networking from Cisco, the company is pitching a tool that makes creating networks in multi-cloud environments as easy as copying and pasting.
• MotorCortex.AI: Co-founded by a Stanford engineering professor and a Carnegie Mellon roboticist, the company is using computer vision, machine learning and robotics to create a fruit packer for packaging lines. Starting with avocados, the company is aiming to tackle the entire packaging side of pick and pack in logistics.
• Resilio: With claims of a 96% effectiveness rate and $35,000 in annual recurring revenue with another $1 million in the pipeline, Resilio is already seeing companies embrace its mobile app that uses a phone’s camera to track stress levels and application-based prompts on how to lower it, according to Alchemist.
• Operant Networks: It’s a long-held belief (of mine) that if computing networks are already irrevocably compromised, the best thing that companies and individuals can do is just encrypt the hell out of their data. Apparently Operant agrees with me. The company is claiming 50% time savings with this approach, and have booked $1.9 million in 2019 as proof, according to Alchemist.
• HPC Hub: HPC Hub wants to democratize access to supercomputers by overlaying a virtualization layer and pre-installed software on underutilized super computers to give more companies and researchers easier access to machines… and they’ve booked $92,000 worth of annual recurring revenue.
• DinoPlusAI: This chip developer is designing a low latency chip for artificial intelligence applications, reducing latency by 12 times over a competing Nvidia chip, according to the company. DinoPlusAI sees applications for its tech in things like real-time AI markets and autonomous driving. Its team is led by a designer from Cadence and Broadcom and the company already has $8 million in letters of intent signed, according to Alchemist.
• Aero Systems West: Co-founders from the Air Force’s Research Labs and MIT are aiming to take humans out of drone operations and maintenance. The company contends that for every hour of flight time, drones require seven hours of maintenance and check ups. Aero Systems aims to reduce that by using remote analytics, self-inspection, autonomous deployment and automated maintenance to take humans out of the drone business.

Watch a live stream of Alchemist’s demo day pitches, starting at 3PM, here.

This week on Extra Crunch, I am exploring innovations in inclusive housing, looking at how 200+ companies are creating more access and affordability. Yesterday, I focused on startups trying to lower the costs of housing, from property acquisition to management and operations.

Today, I want to focus on innovations that improve housing inclusion more generally, such as efforts to pair housing with transit, small business creation, and mental rehabilitation. These include social impact-focused interventions, interventions that increase income and mobility, and ecosystem-builders in housing innovation.

Nonprofits and social enterprises lead many of these innovations. Yet because these areas are perceived to be not as lucrative, fewer technologists and other professionals have entered them. New business models and technologies have the opportunity to scale many of these alternative institutions — and create tremendous social value. Social impact is increasingly important to millennials, with brands like Patagonia having created loyal fan bases through purpose-driven leadership.

While each of these sections could be their own market map, this overall market map serves as an initial guide to each of these spaces.

• Social impact innovations
• Landlord-tenant tools
• Innovations that increase income
• Innovations that increase transit accessibility and reduce parking
• Innovations that improve the ability to regulate housing
• Organizations that support the housing innovation ecosystem
• This is just the beginning
• I’m personally closely watching the following initiatives
• The limitations of technology
• Move fast and protect people

Social impact innovations

These innovations address:

Artificial intelligence applied to information security can engender images of a benevolent Skynet, sagely analyzing more data than imaginable and making decisions at lightspeed, saving organizations from devastating attacks. In such a world, humans are barely needed to run security programs, their jobs largely automated out of existence, relegating them to a role as the button-pusher on particularly critical changes proposed by the otherwise omnipotent AI.

Such a vision is still in the realm of science fiction. AI in information security is more like an eager, callow puppy attempting to learn new tricks – minus the disappointment written on their faces when they consistently fail. No one’s job is in danger of being replaced by security AI; if anything, a larger staff is required to ensure security AI stays firmly leashed.

Arguably, AI’s highest use case currently is to add futuristic sheen to traditional security tools, rebranding timeworn approaches as trailblazing sorcery that will revolutionize enterprise cybersecurity as we know it. The current hype cycle for AI appears to be the roaring, ferocious crest at the end of a decade that began with bubbly excitement around the promise of “big data” in information security.

But what lies beneath the marketing gloss and quixotic lust for an AI revolution in security? How did AL ascend to supplant the lustrous zest around machine learning (“ML”) that dominated headlines in recent years? Where is there true potential to enrich information security strategy for the better – and where is it simply an entrancing distraction from more useful goals? And, naturally, how will attackers plot to circumvent security AI to continue their nefarious schemes?

How did AI grow out of this stony rubbish?

The year AI debuted as the “It Girl” in information security was 2017. The year prior, MIT completed their study showing “human-in-the-loop” AI out-performed AI and humans individually in attack detection. Likewise, DARPA conducted the Cyber Grand Challenge, a battle testing AI systems’ offensive and defensive capabilities. Until this point, security AI was imprisoned in the contrived halls of academia and government. Yet, the history of two vendors exhibits how enthusiasm surrounding security AI was driven more by growth marketing than user needs.

NYC and Boston were handed huge setbacks this week when Amazon and GE decided to bail on their commitments to build headquarters in the respective cities on the same day. But it’s worth pointing out that while these large tech organizations were pulling out, Google was expanding in both locations.

Yesterday, upon hearing about Amazon’s decision to scrap its HQ2 plans in Long Island City, New York City Mayor de Blasio had this to say: “Instead of working with the community, Amazon threw away that opportunity. We have the best talent in the world and every day we are growing a stronger and fairer economy for everyone. If Amazon can’t recognize what that’s worth, its competitors will.” One of them already has. Google had already announced a billion-dollar expansion in Hudson Square at the end of last year.

In fact, the company is pouring billions into NYC real estate, with plans to double its 7,000-person workforce over the next 10 years. As TechCrunch’s Jon Russell reported, “Our investment in New York is a huge part of our commitment to grow and invest in U.S. facilities, offices and jobs. In fact, we’re growing faster outside the Bay Area than within it, and this year opened new offices and data centers in locations like Detroit, Boulder, Los Angeles, Tennessee and Alabama, wrote Google CFO Ruth Porat.”

Just this week, as GE was making its announcement, Google was announcing a major expansion in Cambridge, the city across the river from Boston that is home to Harvard and MIT. Kendall Square is also home to offices from Facebook, Microsoft, IBM, Akamai, DigitalOcean and a plethora of startups.

Google will be moving into a brand new building that currently is home to the MIT Coop bookstore. It plans to grab 365,000 square feet of the new building when it’s completed, and, as in NYC, will be adding hundreds of new jobs to the 1,500 already in place. Brian Cusack, Google Cambridge Site lead points out the company began operations in Cambridge back in 2003 and has been working on Search, Android, Cloud, YouTube, Google Play, Research, Ads and more.

“This new space will provide room for future growth and further cements our commitment to the Cambridge community. We’re proud to call this city home and will continue to support its vibrant nonprofit and growing business community,” he said in a statement.

As we learned this week, big company commitments can vanish just as quickly as they are announced, but for now at least, it appears that Google is serious about its commitment to New York and Boston and will be expanding office space and employment to the tune of thousands of jobs over the next decade.

When Tableau was founded back in 2003, not many people were thinking about artificial intelligence to drive analytics and visualization, but over the years the world has changed and the company recognized that it needed talent to keep up with new trends. Today, it announced it was acquiring Empirical Systems, an early stage startup with AI roots.

Tableau did not share the terms of the deal.

The startup was born just two years ago from research on automated statistics at the MIT Probabilistic Computing Project. According to the company website, “Empirical is an analytics engine that automatically models structured, tabular data (such as spreadsheets, tables, or csv files) and allows those models to be queried to uncover statistical insights in data.”

The product was still in private Beta when Tableau bought the company. It is delivered currently as an engine embedded inside other applications. That sounds like something that could slip in nicely into the Tableau analytics platform. What’s more, it will be bringing the engineering team on board for some AI knowledge, while taking advantage of this underlying advanced technology.

Francois Ajenstat, Tableau’s chief product officer says this ability to automate findings could put analytics and trend analysis into the hands of more people inside a business. “Automatic insight generation will enable people without specialized data science skills to easily spot trends in their data, identify areas for further exploration, test different assumptions, and simulate hypothetical situations,” he said in a statement.

Richard Tibbetts, Empirical Systems CEO, says the two companies share this vision of democratizing data analysis. “We developed Empirical to make complex data modeling and sophisticated statistical analysis more accessible, so anyone trying to understand their data can make thoughtful, data-driven decisions based on sound analysis, regardless of their technical expertise,” Tibbets said in a statement.

Instead of moving the team to Seattle where Tableau has its headquarters, it intends to leave the Empirical Systems team in place and establish an office in Cambridge, Massachusetts.

Empirical was founded in 2016 and has raised $2.5 million.

 Feature Labs, a startup with roots in research begun at MIT, officially launched today with a set of tools to help data scientists build machine learning algorithms more quickly. Co-founder and CEO Max Kanter says the company has developed a way to automate “feature engineering,” which is often a time consuming and manual process for data scientists. “Feature Labs helps… Read More

 IBM and MIT came together today to sign a 10-year, $240 million partnership agreement that establishes the MIT-IBM Watson AI Lab at the prestigious Cambridge, MA academic institution. The lab will be co-chaired by Dario Gil, IBM Research VP of AI and Anantha P. Chandrakasan, dean of MIT’s School of Engineering. Big Blue intends to invest $240 million into the lab where IBM researchers… Read More

 The problem is it’s not an easy undertaking to change the way a large organization operates. Real initiative gets bogged down in politics, hierarchical thinking and institutional inertia. Change requires more than inspiration. It takes hard work — and often the skill of a used car salesman to sell your idea to a reluctant C suite.
Some companies have forward-looking leaders who… Read More

 Long before LinkedIn went public or before a plethora of on-demand startups began washing the Bay Area and other U.S. cities with services at the click of a button, there was this startup from an MIT grad called Assured Labor. David Reich started the company in 2008 to make informal labor markets in Latin America faster and more transparent for low-skilled workers. It was a social enterprise… Read More