alliejanoch

Joined 13 years ago

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26
Submissions
28
Member since
Sep 18
CEO of Mapistry
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Recent submissions

Comment · 1 year ago
Mapistry | Berkeley, CA | Full Time | On-site / Hybrid | Full-stack Software Engineer https://bit.ly/4n1fTVK

Mapistry is looking for an experienced engineer to join our small team, building the environmental compliance command center used by hundreds of manufacturing facilities. If you have always wanted to work on a product that has a meaningful impact on the world and love working at startups, Mapistry could be the place for you.

Please apply with the link above.

Comment · 1 year ago
Mapistry | Berkeley, CA | Full Time | On-site | Full-stack Software Engineer

https://bit.ly/4n1fTVK

Mapistry is looking for an experienced engineer to join our small team, building the environmental compliance command center used by hundreds of manufacturing facilities. If you have always wanted to work on a product that has a meaningful impact on the world and love working at startups, Mapistry could be the place for you.

Please apply with the link above.

Comment · 1 year ago
Mapistry | Full Stack Software Engineer | Berkeley, CA (SF Bay Area) | Hybrid (3 days per week in person)

Mapistry is hiring an experienced Javascript engineer to join our small team, building the environmental compliance command center used by hundreds of manufacturing facilities. If you have always wanted to work on a product that has a meaningful impact on the world and love working at startups, Mapistry could be the place for you.

Apply here: bit.ly/mapistry-software-engineer

Comment · 6 years ago
Comment · 7 years ago
Comment · 8 years ago
Comment · 8 years ago
Mapistry | VP of Demand Gen & SDR | Berkeley, CA | Onsite

Mapistry is SaaS for environmental compliance. Customers include Fortune 100 companies in industries like trash/recycling, consumer products, food processing, mining, etc. We are a team of 8 and recently closed a $2.1M seed round.

VP of Demand Generation: Mapistry is seeking an experienced and passionate Director of Demand Generation to own Mapistry’s demand gen, lead gen, lifecycle marketing and growth. We're looking for someone who is both eager to roll up their sleeves and someone who has the experience to manage the marketing team as Mapistry grows. You’ll be responsible for all our marketing channels including email, SEO, webinars, blog, conferences (both hosted by us and others). One of the leading reason customers use Mapistry is that they know they can trust us to be their environmental compliance experts. This trust starts with marketing. We rely deeply on content marketing and much of our existing marketing strategy revolves around this goal. You’ll need to embrace the challenge of managing and developing highly technical content (with the help of our environmental team).

Sales Development Rep: Mapistry is seeking a Sales Development Representative to join our growing sales team. This is a role for someone who is early in their sales career and seeks an opportunity to grow with our company. You must be comfortable operating in a fast-paced, dynamic environment where change is a constant. The SDR will work closely with the sales, marketing, and product teams and will serve as the first point of contact for prospects.

More details: https://www.mapistry.com/careers

Comment · 9 years ago
Comment · 9 years ago
Stereotypes are not "just patterns". Google "stereotype" and the first definition is "a widely held but fixed and oversimplified image or idea of a particular type of person or thing.".

In this case, the machine is learning, for example, that women do dishes and men drink beer. This isn't based on empirical data and patterns. It comes from the data the algorithm is trained on, which in this case is "...more than 100,000 images of complex scenes drawn from the web, labeled with descriptions". Those descriptions inevitably reflect human stereotypes (which again aren't just patterns). "Both datasets contain many more images of men than women, and the objects and activities depicted with different genders show what the researchers call “significant” gender bias."

Should machines understand that men are more likely to be construction workers than women, I think so. But that doesn't mean that biased data is not a huge problem.

At the very least, we should strive to teach machines to understand the world as it is, not as we view it through flawed, biased eyes. Datasets generated by humans are going to be invariably flawed and far from reality, unless we take careful steps to ensure otherwise. Emphasizing the importance of these datasets, we should be particularly concerned when we are teaching machines to act based on our biases and find that (in the case of this article), the machine is actually learning to amplify our own biases.

Comment · 12 years ago
I have had this experience in Maryland in 2009. Luckily it wasn't a very serious emergency in then end. There was a small explosion in our heater and I smelled smoke, so I wanted the fire department to check it out. I called 911 repeatedly on my cell and repeatedly got a busy signal, finally I went back in the house, got out my computer (before smart phones) and looked up the local fire department number. After they arrived they asked me if I called 911, and I said I couldn't get through and they said I needed to keep trying so they had it for their records?!? The fact that they asked sort of implies that this happens often (or maybe they just knew that I had called directly). It was a weird experience, and turned out that I was lucky that the heater hadn't completely exploded. I definitely don't think I will ever trust 911 again.
Comment · 12 years ago

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