4 Self-driving Semi-truck Stocks for Investors

Autonomous truck stocks

Less than four months ago, we did a deep dive into the first pure play in self-driving semi-truck stocks with a profile on TuSimple (TSP). We found the business to be intriguing but opted to sit on the sidelines for now until the company puts more miles on the road and money into its bank account. We’re bullish on the self-driving theme in general – the technology is there – but it’s going to be a long haul to gain regulatory approvals, mainstream adoption, and profitability. That’s not stopping the race to the public markets. Two more self-driving semi-truck startups are headed in that direction through reverse mergers with special purpose acquisition companies (SPACs): Embark Trucks and Plus. Is either one worth putting on our watchlist?

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Discourse on the philosophy of Artificial Intelligence and the future role of Humanity


I wrote this a while ago, and wanted to post it on here; this didn’t gel with what we were publishing at the time so it got filed away. I hope that you can learn something from it, or at least be entertained. Some citations may have gotten messed up or missatributed, for which I’m very sorry. You can view a nicely formatted copy on Google Docs here. Message me with inquires about the piece; I love to talk about this stuff, and will gladly correct any issues.

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For successful AI Projects, celebrate your graveyard


AI teams invest a lot of rigor in defining new project guidelines. But the same is not true for killing existing projects. In the absence of clear guidelines, teams let infeasible projects drag on for months. AI projects are different from traditional software projects. They have a lot more unknowns: availability of right datasets, model training to meet required accuracy threshold, fairness and robustness of recommendations in production, and many more.

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Effect of Unbalanced and Mixed Dataset on an ML Model

Machine learning requires creating a robust training dataset since the training performs as the seed for subsequent model evaluation. If the training data is corrupted, the model will perform badly as its accuracy will drop. Image classification is a thriving sector for machine learning where a balanced and correct training dataset is extremely important. AI managers as well as data scientists need to ensure the loading of balanced and clean data in the pipeline.

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[Paper Summary] Researchers at Facebook AI, UC Berkeley, and Carnegie Mellon University Announced Rapid Motor Adaptation (RMA), An Artificial Intelligence (AI) Technique

To achieve success in the real world, walking robots must adapt to whatever surfaces they encounter, objects they carry, and conditions they are in, even if they’ve not been exposed to those conditions before. Moreover, to avoid falling and suffering damage, these adjustments must happen in fractions of a second.

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AI Accelerators – Hardware for Artificial Intelligence

AI accelerators

Though CPUs are no longer viable sources of computational power, they were the pioneers. Today, those CPUs are rightfully replaced by GPUs and AI accelerators, specifically designed for large computing. The main features considered while purchasing an AI accelerator are cost, energy consumption, and processing speed.

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How to frame a Product Goal as a Machine Learning problem

Blank slate

Some things are best taught through experience. Such is the case for many tasks in Machine Learning. Machine Learning allows us to learn from large amounts of data and use mathematical formulations to solve problems by optimizing for a given objective. In contrast, traditional programming expects a programmer to write step-by-step instructions to describe how to solve a problem.

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Applying Kaizen & 5S Principles to external Data Acquisition

Partnership graphic

The key to solving any analytical problem is to have the right data. Data is an asset. One that forward-thinking organizations seek out just as actively as they would revenue streams or new customers. And for good reason — with relevant data, organizations make smarter decisions and solve critical business challenges. Today, “good analytics” means going well beyond a few commonly available algorithms or a dashboard to showcase internal data.

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