AI is moving from data centers to devices, making everything from phones to tractors faster and more private. These newfound smarts also come with pitfalls.

If you think of AI as something futuristic and abstract, start thinking different.

We’re now witnessing a turning point for artificial intelligence, as more of it comes down from the clouds and into our smartphones and automobiles. While it’s fair to say that AI that lives on the “edge” — where you and I are — is still far less powerful than its datacenter-based counterpart, it’s potentially far more meaningful to our everyday lives.

One key example: This fall, Apple’s Siri assistant will start processing voice on iPhones.

Right now, even your request to set a timer is sent as an audio recording to the cloud, where it is processed, triggering a response that’s sent back to the phone.

By processing voice on the phone, says Apple, Siri will respond more quickly. This will only work on the iPhone XS and newer models, which have a compatible built-for-AI processor the company calls a “neural engine.”

People might also feel more secure knowing that their voice recordings aren’t being sent to unseen computers in faraway places.

Google actually led the way with on-phone processing: In 2019, it introduced a Pixel phone that could transcribe speech to text and perform other tasks without any connection to the cloud.

One reason Google decided to build its own phones was that the company saw potential in creating custom hardware tailor-made to run AI, says Brian Rakowski, product manager of the Pixel group at Google.

These so-called edge devices can be pretty much anything with a microchip and some memory, but they tend to be the newest and most sophisticated of smartphones, automobiles, drones, home appliances, and industrial sensors and actuators.

Edge AI has the potential to deliver on some of the long-delayed promises of AI, like more responsive smart assistants, better automotive safety systems, new kinds of robots, even autonomous military machines.

The challenges of making AI work at the edge — that is, making it reliable enough to do its job and then justifying the additional complexity and expense of putting it in our devices — are monumental.

Existing AI can be inflexible, easily fooled, unreliable and biased. In the cloud, it can be trained on the fly to get better — think about how Alexa improves over time. When it’s in a device, it must come pre-trained, and be updated periodically.

Yet the improvements in chip technology in recent years have made it possible for real breakthroughs in how we experience AI, and the commercial demand for this sort of functionality is high.

From swords to plowshares

Shield AI, a contractor for the Department of Defense, has put a great deal of AI into quadcopter-style drones which have already carried out — and continue to be used in — real-world combat missions.

One mission is to help soldiers scan for enemy combatants in buildings that must be cleared.

The DoD has been eager to use the company’s drones, says Shield AI’s co-founder, Brandon Tseng, because even if they fail, they can be used to reduce human casualties.

“In 2016 and early 2017, we had early prototypes with something like 75% reliability, something you would never take to market, and the DoD were saying, ‘We’ll take that overseas and use that in combat right now’,” Mr. Tseng says.

When he protested that the system wasn’t ready, the response from within the military was that anything was better than soldiers going through a door and being shot.

In a combat zone, you can’t count on a fast, robust, wireless cloud connection, especially now that enemies often jam wireless communication and GPS signals. When on a mission, processing and image recognition must occur on the company’s drones themselves.

Shield AI uses a small, efficient computer made by Nvidia, designed for running AI on devices, to create a quadcopter drone no bigger than a typical camera-wielding consumer model.

The Nova 2 can fly long enough to enter a building, and use AI to recognize and examine dozens of hallways, stairwells and rooms, cataloging objects and people it sees along its way.

Meanwhile, in the town of Salinas, Calif., birthplace of Grapes of Wrath author John Steinbeck and an agricultural center to this day, a robot the size of an SUV is spending this year’s growing season raking the earth with its 12 robotic arms.

Made by FarmWise Labs Inc., the robot trundles along fields of celery as if it were any other tractor. Underneath its metal shroud, it uses computer vision and an edge AI system to decide, in less than a second, whether a plant is a food crop or a weed, and directs its plow-like claws to avoid or eradicate the plant accordingly.

FarmWise’s huge, diesel robo-weeder can generate its own electricity, enabling it to carry a veritable supercomputer’s worth of processing power — four GPUs and 16 CPUs which together draw 500 watts of electricity.

In our everyday lives, things like voice transcription that work whether or not we have a connection, or how good it is, could mean shifts in how we prefer to interact with our mobile devices.

Getting always-available voice transcription to work on Google’s Pixel phone “required a lot of breakthroughs to run on the phone as well as it runs on a remote server,” says Mr. Rakowski.

Google has almost unlimited resources to experiment with AI in the cloud, but getting those same algorithms, for everything from voice transcription and power management to real-time translation and image processing, to work on phones required the introduction of custom microprocessors like the Pixel Neural Core, he adds.

Turning cats into pure math

What nearly all edge AI systems have in common is that, as pre-trained AI, they are only performing “inference,” says Dennis Laudick, vice president of marketing for AI and machine learning at Arm Holdings, which licenses chip designs and instructions to companies such as Apple, Samsung, Qualcomm, Nvidia and others.

Generally speaking, machine-learning AI consists of four phases:

Data is captured or collected: Say, for example, in the form of millions of cat pictures.

Humans label the data: Yes, these are cat photos.

AI is trained with the labeled data: This process selects for models that identify cats.

Then the resulting pile of code is turned into an algorithm and implemented in software: Here’s a camera app for cat lovers!

(Note: If this doesn’t exist yet, consider it your million-dollar idea of the day.)

The last bit of the process — something like that cat-identifying software — is the inference phase.

The software on many smart surveillance cameras, for example, is performing inference, says Eric Goodness, a research vice president at technology-consulting firm Gartner.

These systems can already identify how many patrons are in the restaurant, if any are engaging in undesirable behavior, or if the fries have been in the fryer too long.

It’s all just mathematical functions, ones so complicated that it would take a monumental effort by humans to write them, but which machine-learning systems can create when trained on enough data.

Robot pratfalls

While all of this technology has enormous promise, making AI work on individual devices, whether or not they can connect to the cloud, comes with a daunting set of challenges, says Elisa Bertino, a professor of computer science at Purdue University.

Modern AI, which is primarily used to recognize patterns, can have difficulty coping with inputs outside of the data it was trained on. Operating in the real world only makes it tougher — just consider the classic example of a Tesla that brakes when it sees a stop sign on a billboard.

To make edge AI systems more competent, one edge device might gather some data but then pair with another, more powerful device, which can integrate data from a variety of sensors, says Dr. Bertino.

If you’re wearing a smartwatch with a heart-rate monitor, you’re already witnessing this: The watch’s edge AI pre-processes the weak signal of your heart rate, then passes that data to your smartphone, which can further analyze that data — whether or not it’s connected to the internet.

The overwhelming majority of AI algorithms are still trained in the cloud. They can also be retrained using more or fresher data, which lets them continually improve.

Down the road, says Mr. Goodness, edge AI systems will begin to learn on their own — that is, they’ll become powerful enough to move beyond inference and actually gather data and use it to train their own algorithms.

AI that can learn all by itself, without connection to a cloud superintelligence, might eventually raise legal and ethical challenges.

How can a company certify an algorithm that’s been off evolving in the real world for years after its initial release, asks Dr. Bertino.

And in future wars, who will be willing to let their robots decide when to pull the trigger? Whoever does might end up with an advantage — but also all the collateral damage that happens when, inevitably, AI makes mistakes.

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Most of the big data analytics we perform centers around what we expect it to be

“Why is the product unsuccessful? Did we not plan it after going through big data analytics?” asked a company executive. This is not the first company or the first time such confusion has happened. If we take a close look around, most of the data analytics we perform centers around the concept we expect them to be, leading to massive setbacks. Yes, it is true. Even though we praise big data for being the accelerator of every decision, we can’t deny the fact that it can be misleading at times.

Big data is more than just structured and unstructured data. It is seen as a base ingredient of all decision-making processes. For the past two decades, ever since mobile phones came into existence and technology evolved exponentially, It became a critical part of every business operation. Big data in business is a very common substance that executives and employees used to get insights into their market performance. Technology experts are all praises about data, with many touting it as the best thing that has happened to humankind. But the truth is a little twisted. When data is used correctly, it opens the door to double or triple-fold the revenue in minimum time. Unfortunately, it can also be misleading, draining the company’s efforts to go down the gutter. A report by Blazent, an IT intelligence company unravels many findings of big data disadvantages. It shows that around 42% of executives state that misuse of data can impair revenues and 39% said this can be deteriorating for correct decision-making. Henceforth, this article takes you through how big data is misused and what can be done to patch the gap.

Drawing an example from political endeavor

Political circle, especially, presidential elections were heavily relying on big data outcomes. Of course, curiosity didn’t let us be silent. It almost became a custom to know the result through pre-poll analysis. But if we look back at the records, they were not always right. Most recently, the 2016 election that gave Hillary Clinton a 90% chance of victory ended up making Donald Trump the President of the United States. This could either be because of a crack in the data or the data itself was faulty. The big data analytics clearly depicts the fact that human nature as of yet, cannot be reduced to a series of ones and zeros.

Moving on to big data in business and its disadvantages 

Businesses are increasingly relying on big data today. Starting from making simple decisions on marketing and promotion to big ones like where to invest and how to gain more revenues, literally, everything revolves around data. Unfortunately, business executives are unaware that technological innovation is a double-edged sword. If it is not used for good intent, It can wreak havoc.

Datasets are huge and are spread across many disparate locations and diverse forms. Henceforth, business organizations are unaware of whether the data is clean, accurate, manageable, and usable. Besides, some of the data are also manually entered into the system, prompting human errors. While such mismatched data are processed together, it leads to serious negatives and misleading outcomes. However, companies, unaware of the datasets condition take the result as everything and proceed with it.

Businesses are increasingly relying on algorithms to sort company issues. Brian Bergstein of MIT Technology Review suggests that the growing reliance on big data in business is creating a corporate bubble of overconfidence. But why are algorithms unreliable? Even though algorithms are computer-based, they have their own form of risk since they are ‘created by people and they contain interferences and assumptions coded in.’ These coded-in values shape the outputs like computer-generated predictions, recommendations, and simulations.

Finally, one of the biggest setbacks of big data analytics is people’s perceptions. While company executives have a perception on certain products or product developments, the consumers’ viewpoint might vary. But this goes unnoticed when companies focus on delivering their viewpoint to customers without addressing their concerns. Organizations design questions that they want to ask. It is solely on the executive’s perception of what clients needed to answer. They weren’t reflecting on what clients wanted to express. As a result, business takes the wrong path in the name of following big data insights.

What can be done?

Listen to customers. It is the only option to keep away misconceptions. Even though engaging with customers and having a face-to-face or virtual conversation may not be as exciting as compiling big data answers, they reflect on people’s thoughts. When we ask random questions and let them talk, they talk their hearts out and say things that might build the stairs for the organization’s success. For example, Toyota and Adobe are two such companies that go for people’s view than big data decision-making.

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