{"id":25349,"date":"2021-07-21T04:48:22","date_gmt":"2021-07-21T04:48:22","guid":{"rendered":"https:\/\/rbsitsoftwaresolution.com\/blog\/how-we-built-an-ai-unicorn-in-6-years.html"},"modified":"2021-07-21T04:48:22","modified_gmt":"2021-07-21T04:48:22","slug":"how-we-built-an-ai-unicorn-in-6-years","status":"publish","type":"post","link":"https:\/\/www.rbsitsoftwaresolution.in\/blog\/how-we-built-an-ai-unicorn-in-6-years.html","title":{"rendered":"How we built an AI unicorn in 6 years"},"content":{"rendered":"

Today, Tractable is worth<\/a><\/span> $1 billion. Our AI is used by millions of people across the world to recover faster from road accidents, and it also helps recycle as many cars as Tesla puts on the road.<\/p>\n

And yet six years ago, Tractable was just me and Raz (Razvan Ranca, CTO), two college grads coding in a basement. Here\u2019s how we did it, and what we learned along the way.<\/p>\n

Build upon a fresh technological breakthrough<\/h2>\n

In 2013, I was fortunate to get into artificial intelligence (more specifically, deep learning) six months before it blew up internationally. It started when I took a course on Coursera called \u201cMachine learning with neural networks\u201d by Geoffrey Hinton. It was like being love struck. Back then, to me AI was science fiction, like \u201cThe Terminator.\u201d<\/p>\n<\/p>\n

But an article in the tech press said the academic field was amid a resurgence. As a result of 100x larger training data sets and 100x higher compute power becoming available by reprogramming GPUs (graphics cards), a huge leap in predictive performance had been attained in image classification a year earlier. This meant computers were starting to be able to understand what\u2019s in an image \u2014 like humans do.<\/p>\n

The next step was getting this technology into the real world. While at university \u2014 Imperial College London \u2014 teaming up with much more skilled people, we built a plant recognition app<\/a> with deep learning. We walked our professor through Hyde Park, watching him take photos of flowers with the app and laughing from joy as the AI recognized the right plant species. This had previously been impossible.<\/p>\n

I started spending every spare moment on image classification with deep learning. Still, no one was talking about it in the news \u2014 even Imperial\u2019s computer vision lab wasn\u2019t yet on it! I felt like I was in on a revolutionary secret.<\/p>\n

Looking back, narrowly focusing on a branch of applied science undergoing a breakthrough paradigm shift that hadn\u2019t yet reached the business world changed everything.<\/p>\n

Search for complementary co-founders who will become your best friends<\/h2>\n

I\u2019d previously been rejected from Entrepreneur First<\/a> (EF), one of the world\u2019s best incubators, for not knowing anything about tech. Having changed that, I applied again.<\/p>\n

The last interview was a hackathon, where I met Raz. He was doing machine learning research at Cambridge, had topped EF\u2019s technical test, and published papers on reconstructing shredded documents and on poker bots that could detect bluffs. His bare-bones webpage<\/a> read: \u201cI seek data-driven solutions to currently intractable problems.\u201d Now that had a ring to it (and where we\u2019d get the name for Tractable).<\/p>\n

That hackathon, we coded all night. The morning after, he and I knew something special was happening between us. We moved in together and would spend years side by side, 24\/7, from waking up to Pantera in the morning to coding marathons at night.<\/p>\n

But we also wouldn\u2019t have got where we are without Adrien (Cohen, president), who joined as our third co-founder right after our seed round. Adrien had previously co-founded Lazada, an online supermarket in South East Asia like Amazon and Alibaba, which sold to Alibaba for $1.5 billion. Adrien would teach us how to build a business, inspire trust and hire world-class talent.<\/p>\n

Find potential customers early so you can work out market fit<\/h2>\n

Tractable started at EF with a head start \u2014 a paying customer. Our first use case was \u2026 plastic pipe welds.<\/p>\n

It was as glamorous as it sounds. Pipes that carry water and natural gas to your home are made of plastic. They\u2019re connected by welds (melt the two plastic ends, connect them, let them cool down and solidify again as one). Image classification AI could visually check people\u2019s weld setups to ensure good quality. Most of all, it was real-world value for breakthrough AI.<\/p>\n

And yet in the end, they \u2014 our only paying customer \u2014 stopped working with us, just as we were raising our first round of funding. That was rough. Luckily, the number of pipe weld inspections was too small a market to interest investors, so we explored other use cases \u2014 utilities, geology, dermatology and medical imaging.<\/p>\n

Article source: http:\/\/feedproxy.google.com\/~r\/techcrunchIt\/~3\/u0Y3331uZ4w\/<\/p>","protected":false},"excerpt":{"rendered":"

Today, Tractable is worth $1 billion. Our AI is used by millions of people across the world to recover faster from road accidents, and it also helps recycle as many <\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[19],"tags":[25],"class_list":["post-25349","post","type-post","status-publish","format-standard","hentry","category-enterprise-software","tag-enterprise"],"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t