What Makes Successful AI

In the last blog, we talked about the seven patterns of AI and how companies like Tesla use them for a better user experience. However, in our recent webinar, we also discuss how companies succeed and fail using these AI systems. While implementing data solutions can help businesses thrive, they can also get in the way of actual learning when misused.

So many startups and C-suite executives want to implement the “latest and greatest” without thinking about what their AI is trying to solve. Instead, they envision the solution and build around it, rather than building a system around the problem and allowing the data to give them the answer. For example, many people envisioned the first self-driving cars with human-like robots in the driver’s seat. In contrast, the first successful Tesla system built the AI into the vehicles themselves.

By focusing on the problem rather than the envisioned solution, Tesla created a mass-market self-driving car that people actually wanted. Likewise, your business can do the same if your AI has these three elements of success:

  • The proper structure – The structure of your data solution is entirely dependent on the type of data you’re collecting. For example, if you’re processing images of faces, you need a tool built specifically for that, instead of trying to retrofit one built for other purposes.
  • Patterns – For AI to be agile and robust in a commercial setting, you need multiple data sets that include slight deviations, to establish patterns. Therefore, the more quality data you provide, the better your machines can detect patterns and grow in the environment. Click here to learn more about what constitutes quality data.
  • Features – The more varied your data set, the more you’ll potentially be able to learn. For instance, if you’re building AI to recognize facial features, adding a variety of ethnicities, ages, and expressions is essential to creating a robust system. Without variation, you’re limiting your learning.

Without these elements, your data won’t be capable of the work you require. By creating a robust and quality data set, you can reduce the amount of uncertainty in your decision-making process, and make the uncertainty still present negligible. You don’t need exact solutions or the perfect data set, but you need to identify features, patterns, and the proper structure to obtain your goal. 

Seven Patterns of AI and How To Use Them For Your Business

Fifty years ago, the vision of AI looked more like a sci-fi fantasy. No one predicted the power of AI to involve autonomous vehicles or highly personalized conversation-based communication between humans and machines. Despite the wild advancements we’ve made with artificial intelligence, many startups still underestimate the abilities or the true applications where artificial intelligence thrives.

In the webinar Data Science Essentials for Startups: All About AI we looked into the different ways in which AI is being used at Tesla, the electric vehicle, solar, and clean energy company paving the path toward a smarter future.

Most people think of Tesla as an automotive company, but we see them as a robotics and software company that happens to specialize in transportation. They are also one of the most successful companies of our time. In 2020 alone, they sold over 500,000 vehicles and generated $31.5B in revenue.

Their autopilot feature, which we’ll be discussing in this article, has had over 3 billion miles on the ground (and trillions of miles in simulations), making it the most advanced and researched autonomous vehicle on the market. Tesla does this by using the seven AI patterns listed below to create their total user experience:

  1. Autonomous systems
    AI is often used to create autonomous systems which can navigate and learn without human involvement. For example, this is the hallmark of the Tesla Autopilot feature – cars are able to drive and even pick up their owners with little-to-no human interaction.
  2. Goal-driven systems
    Goal-driven AI is used to test technologies in simulated environments to improve product features or create novel solutions. For example, Tesla uses goal-driven AI to test each of its electric models to prepare vehicle designs for the unpredictable human environment. However, this can be used for testing and training in any environment.
  3. Recognition
    Recognition is another popular use for AI, especially with commonly used technologies such as facial detection or security. For example, Tesla uses recognition AI in their “Sentry Mode” feature, which helps to protect vehicles from theft or damage when the owner is away. It can alert the owner of potential dangers through a livestream camera, and therefore, deters break-ins better than nearly any other car on the market.
  4. Predictive analytics and decisions
    Predictive AI is the biggest contributor to innovation and improved decision-making for companies of all kinds. For instance, Tesla uses predictive analytics to enhance route options which can help improve the overall driver experience. Their system also predicts areas of high pedestrian traffic, so that it can help reduce the likelihood of accidents.
  5. Hyper personalization
    Hyper personalization has been a hot topic in the news regarding online and social media marketing, but it also has applications in AI. For example, Tesla hyper-personalizes the driving experience by adding driving preferences to seat position, temperature, music, and more. As a result, they build a better car for the individual driver, creating customer loyalty and edging out the competition.
  6. Conversation & human interactions
    Most people associate conversation-based AI with products like Amazon Alexa and Google Assistant, but this technology will likely be widespread in the next decade. Tesla incorporates voice commands for features like music, temperature, and route options, as an example, to make the driving experience hassle-free.
  7. Patterns and anomalies
    AI can quickly detect patterns for different data sets, and therefore identify anomalies that can be useful for companies to understand. For example, this has helped Tesla designate parking lots and residential areas as higher-risk for pedestrian crossings while maintaining vigilance for anomalies like deer and bicycles on the highways.

Your startup may not utilize every model of AI for your applications, but by understanding the different options, you can build a better product with the data you have. Echelon DS is helping to develop systems in which companies can use AI and big data to accelerate their startup success, without high costs. If you’d like to learn more about what we’re doing with AI, contact us.

Data Pitfalls Leadership Should Avoid When Implementing Machine Learning

Data science opens up doors we’ve never had in the startup world. Today, we can learn faster, make higher quality decisions, and serve customers in hyper-personalized ways that were not possible a few years ago. However, open access to data tools does not always help entrepreneurs to make better businesses. So many C-suite executives get entranced in the magic of data without understanding what they truly need to make their solutions work. 

As tech enthusiasts and early adopters, we always want to use the “latest and greatest” in data science technology. However, a few problems with this mindset cause leaders to backtrack instead of move forward. Here’s why:

R&D from academia is not always allowed for commercial use.
Many cutting-edge data solutions that are accessible to smaller startups (and not funded by independent R&D from billion-dollar multinationals) are not always applicable or allowed for commercial use. They may require expensive licensing or are in too early a state to be implementable for your business.

The robustness of novel solutions is unproven.
The lab environment is drastically different from the commercial environment, which means that many data solutions published from academic research centers are not applicable for a living, breathing market.

Solutions are costly to implement.
Most of the data solutions available for commercial use are costly to implement, either because of the licensing or special equipment and/or the software required to use them. Again, these R&D projects are built for the lab environment, which means that using them for your startup with a bare-bones infrastructure can be difficult.

You may be forcing a problem into a solution that’s unrelated.
Leaders that get wrapped up in the “latest and greatest” technologies want to use them regardless of how applicable they are to the startups’ purpose and problems at hand. As a result, they may invest in data solutions that aren’t well-suited for their needs solely for the sake of being on the cutting edge and will get worse outcomes because of it.

By the time you can use it, there’s a new “latest and greatest.”
The cutting-edge is an ever-moving target. Even thought-leaders and cutting edge corporations must invest years or decades into technologies to make them worthwhile, and in the meantime, a new latest and greatest will inevitably appear.

For these reasons, being an early adopter of data science solutions is risky for startups. In many cases, it’s best to use well-established avenues for data acquisition and modeling that can bring in the results you want. However, you’ll inevitably miss out on features and abilities that could benefit your business if you play it too safe.

That is why Echelon DS builds custom data applications that serve the exact needs of their startups, using both established and experimental methods for data processing that make sense for your business. Furthermore, we build these solutions collaboratively to help cut costs and accelerate decision-making for startups in all sectors. With this hybrid solution, you can avoid the pitfalls of using newer data technology, without the fear of being left behind.

To learn more about our solutions for startups, click here.