If artificial intelligence confuses you, think about what happens when it’s. For context, consider the London-based VC firm MMC who found that don’t use AI in any tangible way. MMC (and TheVerge) are saying that companies just want to take advantage of the AI hype. Meaning that the company or startup “talks” about AI, but they are unwilling or unable to put in the resources to deploy the process, store the data, or make any meaningful use of the information. So begs the question, is artificial intelligence still relevant for startups?
AI’s promise may be further ahead than its practical reality for young companies and startups that face an uphill grind against their larger peers. Larger entities and new startups have the same needs.
To gather and organize vast amounts of training data needed to build effective AI solutions is cost prohibitive for a startup. Expectations always outpace reality, but that isn’t still a bad thing. There are plenty of entrepreneurs diligently working toward a better future. But in high-tech businesses, it’syou build, promote, or invest in — is authentic.
The Substantive Role of AI
The ability to automate responsibilities and streamline efficiency makesan attractive productivity option for businesses. It shouldn’t be a surprise that the buzz surrounding AI/machine learning and early stage startups peaked in 2018. So much so, that there was a half-joking consensus that adding either to your pitch deck meant an immediate .
But the standard for “AI-powered” varies widely — and not only in startups. From a“machine learning” means introducing data into a neural network, so the mathematical model learns to recognize patterns.
Once that AI foundation is in place, the network learns to recognize categorization, transformation, and even prediction.
These capabilities create four startup types, each bringing something different to the AI table:
• Aspirational: Most startups fit into this category, and their founders claim AI/ML deep in the pitch deck. But what those companies mean is that once they’ve found product-market fit and have 500,000 users creating millions of data points, they’ll be able to leverage AI to generate useful insights. None of these startups do any meaningful machine learning work before a Series B funding round.
• Specialized: These startups apply AI solutions to specific industry problems. Examples include, which improves delivery fleets; , which creates cashier-less stores; and , which trains neural nets to identify skin disease better than primary care physicians.
• Foundational: These AI startups build the tools that the AI industry will someday use. Information that will “someday” be used typically means more nuanced API designs or math-heavy algorithmic research. These companies are laying the foundation that the next generation of specialized AI startups will be built upon.
• Opportunistic: AI startups use out-of-the-box machine learning APIs from established tech companies to add a little extra oomph to their products. Identifying whether a cat is in a photo oraren’t core to these businesses, but those qualities can distinguish products from the competition. These startups use AI as a standard part of their tool kits, and they represent the future of how most businesses ultimately will use AI.
Understanding which ecosystem a startup fits into is the key to building an authentic AI enterprise.
This pragmatic approach means recognizing when you’re the market leader introducing AI to a new segment and when you’re simply building something that eventually will be AI-ready.
You’re not just adding “AI/ML” to your business to take advantage of a trend. You’re leveraging technology to solve a real problem, which is what makes a business viable.
When Is AI Relevant to Startups?
When assessing a company’s AI-readiness, it’s important to determine the purpose it will serve. If it’s a support beam for a business, large amounts of data and an understanding of that data’s value to an existing industry are needed.
AI is not a secret sauce — data is the sauce. To that end, it’s crucial to understand how much data good ML and AI requires. The data source is what ultimately drives the ecosystem, and it must be well-structured and optimized. This data also has to be stored securely.
For example, a startup could leverage AI/ML to analyze the entire Twitter firehose to measure influence, conduct sentiment analysis, or even surface brand recognition to tie back to a Super Bowl ad campaign. There are all sorts of companies doing these things, and it’s only made possible by a platform like Twitter, which has troves of data.
Wherever there’s data that isn’t being thoughtfully examined, there’s a massive opportunity for AI disruption.
The larger the data set you can gain access to, the more interesting work your platform can do. Look at CentralSquare Technologies, an AI startup that connects emergency call centers around the country. Emergency dispatch centers ran independently for a long time before startups like CentralSquare came along. The company connects aboutacross major metropolitan areas such as Los Angeles, Houston, Atlanta, and Dallas. This decreases 911 response times in a world where your phone, car, or smartwatch will likely be calling far from your home area code.
, along with Esri and Geospiza, is doing something similar with FEMA data. These organizations are creating AI dashboards using predictive analytics around disaster and emergency response. That team could tell you the best bet when responding to a tornado, hurricane, or earthquake plaguing a city using predictive analysis. Data-driven AI can guide emergency management services through tough situations.
Just remember that not all AI companies are created equal, and it’s very possible you’ll encounter an AI company with no real AI.
The company is probably not a scam — they may be “bragging.” Many companies are just using the term “articifial intelligence” because they plan to use this technology at some point. They just can’t afford actually to do the work AI entails – now. Due diligence can uncover what’s genuinely artificial in that company. As long as you understand what you’re seeing — and seeking — then you will be less likely to have a problem.
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