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You’re not an AI company until you’re a full-stack company

From Siri and Alexa becoming household names to Apple recently announcing its new HomePod smart speaker, artificial intelligence finally seems to have made the jump from the world of futuristic science fiction straight into our own homes. The variety of tasks that AI creations are taking on is increasing. AI can vacuum our floors, create expensive works of art, and even debate the meaning of life.

With AI technology permeating the public consciousness, tech companies are eager to ride the hype and emphasize the AI aspects of their products. Everyone is claiming to be an AI company, showcasing their neural networks or their bots. But with a narrow and specialized focus, those companies are often missing the big picture. The companies that will rise to the top and stay there are full-stack AI companies.

AI is a holistic process

To build a meaningful AI system, you have to have a big data practice, a software engineering practice, and a user experience design practice. Only then can your AI evolve into a useful and practical tool that can communicate with other applications. You also need to create a feedback loop to determine whether the decisions that your AI system is making are actually helpful to the people using it.

I saw this for myself when I started my career as a knowledge engineer, focused on creating the backend rules and algorithms that give AI systems the ability to “think.” I quickly realized that in order to deliver results, knowledge engineering isn’t enough: I also needed to understand software engineering, the front-facing aspects of the trade that made our AI systems useful and usable to customers. If I wanted to be effective, I needed mastery over the complete, end-to-end solution.

Companies looking to develop successful AI will also need to master every level of the solution: big data, analytics, and user experience (UX). A bot or an algorithm can’t and shouldn’t just stand on its own.

Trend-driven AI misses the big picture

The market has witnessed enthusiasm for the AI trend before, and we can learn from it. Prior to 1990, venture funding poured into artificial intelligence. As early as 1986, university researchers in Munich successfully tested self-driving vans.

In the 1990s, though, the fickle market grew tired of AI. The bubble for AI products burst. Even mentioning the word in a company’s description would hurt a startup’s valuation. That doesn’t mean companies stopped working on AI advancements — they just stopped calling it that. They replaced the terminology with catchphrases like “big data” or “intelligent algorithms” that were still pieces of the AI stack but didn’t take into consideration the full picture.

This focus on just one facet of the science puts companies at risk of missing AI’s larger potential. In business, this translates to missed opportunities to build a lasting product, rather than a trend-driven one.

Moving forward with a full-stack approach

For artificial intelligence to truly become mainstream, it needs to work with and fit into the universe that people already live in. This is rough news for industry leaders who place AI at the center of its own universe, envisioning it to be a standalone source of light around which everything else must revolve. These companies tend to view a bot or an algorithm as the end game, not realizing that these products on their own are not actually solving problems.

People are connected to technology like never before, with end-points from our wrists to our thermostats to industrial assembly lines. That means AI finally has the network it needs to reach its full potential — if developers can successfully integrate it in order to improve and streamline the devices we are already using.

Until recently, only the largest companies and institutions had the technological capabilities to work with and analyze big data by reading, interpreting, and sending huge amounts of quality information. Today, though, cloud computing and advanced computing power make working with large datasets easier, more affordable, and more accessible.

Additionally, working on the user experience aspect of artificial intelligence has recently become within reach, given the proliferation of API building blocks for user-friendly packages like responsive mobile apps. Previously, these would have to be coded from scratch.

Why full-stack companies are still rare

While recent progress serves to level the playing field somewhat, it doesn’t mean an AI stack is easy to build.

Data, in particular, proves to be difficult for many companies hoping to join the full-stack club. It’s hard to acquire, it’s messy, it’s inaccurate, and it’s incomplete. Analysts can look at pools of data and come up with correlations and insights, but knowing how those insights apply to real-life problems is yet another part of the stack: user experience (UX). AI without UX design as a core competency will only have a tiny impact — namely, it will answer the question of  “what does the data say?” but not “so now what do we do?”

Full stack is the way of the future

As the industry works to integrate artificial intelligence into every aspect of our daily technology use, a full-stack AI company will be best positioned to thrive. This is because companies that deliver a single AI component will become commodities for end users.

In contrast, full-stack companies will have the knowledge to see how their product fits into a complete solution. They will also be able to take advantage economically of the vertical integration that comes along with that knowledge.

John Price is the chief executive officer at Vast, a company that provides a platform to support big data experiences for automotive and real estate.

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About Ms. A. C. Kennedy

Ms. A. C. Kennedy
My name is Ms A C Kennedy and I am a Health practitioner and Consultant by day and a serial blogger by night. I luv family, life and learning new things. I especially luv learning how to improve my business. I also luv helping and sharing my information with others. Don't forget to ask me anything!

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Existing EV batteries could be recharged five times faster

Lithium-ion batteries have massively improved in the last half-decade, but there are still issues. The biggest, especially for EVs, is that charging takes too long to make them as useful as regular cars for highway driving. Researchers from the University of Warwick (WMG) have discovered that we may not need to be so patient, though. They developed a new type of sensor that measures internal battery temperatures and discovered that we can probably recharge them up to five times quicker without overheating problems.

Overcharging a lithium-ion battery anode can lead to lithium buildup, which can break through a battery's separator, create a short-circuit and cause catastrophic failure. That can cause the electrolyte to emit gases and literally blow up the battery, so manufacturers impose strict charging power limits to prevent it.

Those limits are based on hard-to-measure internal temperatures, however, which is where the WMG probe comes in. It's a fiber optic sensor, protected by a chemical layer that can be directly inserted into a lithium-ion cell to give highly precise thermal measurements without affecting its performance.

The team tested the sensor on standard 18650 li-ion cells, used in Tesla's Model S and X, among other EVs. They discovered that they can be charged five times faster than previously thought without damage. Such speeds would reduce battery life, but if used judiciously, the impact would be minimized, said lead researcher Dr. Tazdin Amietszajew.

Faster charging as always comes at the expense of overall battery life but many consumers would welcome the ability to charge a vehicle battery quickly when short journey times are required and then to switch to standard charge periods at other times.

There's still some work to do. While the research showed the li-ion cells can support higher temperatures, EVs and charging systems would have to have "precisely tuned profiles/limits" to prevent problems. It's also not clear how battery makers would install the sensors in the cells.

Nevertheless, it shows a lot of promise for much faster charging speeds in the near future. Even if battery capacities stayed the same, charging in 5 minutes instead of 25 could flip a lot of drivers over to the green side.

Via: Clean Technica

Source: University of Warwick