Tag Archives: Processing

AIoT: How the Intersection of AI and IoT Will Drive Innovation for Decades to Come

We have covered the evolution of the Internet of Things (IoT) and Artificial Intelligence (AI) over the years as they have gained prominence. IoT devices collect a massive amount of data. Cisco projects by the end of 2021, IoT devices will collect over 800 zettabytes of data per year. Meanwhile, AI algorithms can parse through big data and teach themselves to analyze and identify patterns to make predictions. Both technologies enable a seemingly endless amount of applications retained a massive impact on many industry verticals.

What happens when you merge them? The result is aptly named the AIoT (Artificial Intelligence of Things) and it will take IoT devices to the next level.

WHAT IS AIOT?

AIoT is any system that integrates AI technologies with IoT infrastructure, enhancing efficiency, human-machine interactions, data management and analytics.

IoT enables devices to collect, store, and analyze big data. Device operators and field engineers typically control devices. AI enhances IoT’s existing systems, enabling them to take the next step to determine and take the appropriate action based on the analysis of the data.

By embedding AI into infrastructure components, including programs, chipsets, and edge computing, AIoT enables intelligent, connected systems to learn, self-correct and self-diagnose potential issues.

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One common example comes in the surveillance field. Surveillance camera can be used as an image sensor, sending every frame to an IoT system which analyzes the feed for certain objects. AI can analyze the frame and only send frames when it detects a specific object—significantly speeding up the process while reducing the amount of data generated since irrelevant frames are excluded.

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While AIoT will no doubt find a variety of applications across industries, the three segments we expect to see the most impact on are wearables, smart cities, and retail.

WEARABLES

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The global wearable device market is estimated to hit more than $87 billion by 2022. AI applications on wearable devices such as smartwatches pose a number of potential applications, particularly in the healthtech sector.

Researchers in Taiwan have been studying the potential for an AIoT wearable system for electrocardiogram (ECG) analysis and cardiac disease detection. The system would integrate a wearable IoT-based system with an AI platform for cardiac disease detection. The wearable collects real-time health data and stores it in a cloud where an AI algorithm detects disease with an average of 94% accuracy. Currently, Apple Watch Series 4 or later includes an ECG app which captures symptoms of irregular, rapid or skipped heartbeats.

Although this device is still in development, we expect to see more coming out of the wearables segment as 5G enables more robust cloud-based processing power, taking the pressure off the devices themselves.

SMART CITIES

We’ve previously explored the future of smart cities in our blog series A Smarter World. With cities eager to invest in improving public safety, transport, and energy efficiency, AIoT will drive innovation in the smart city space.

There are a number of potential applications for AIoT in smart cities. AIoT’s ability to analyze data and act opens up a number of possibilities for optimizing energy consumption for IoT systems. Smart streetlights and energy grids can analyze data to reduce wasted energy without inconveniencing citizens.

Some smart cities have already adopted AIoT applications in the transportation space. New Delhi, which boasts some of the worst traffic in the world, features an Intelligent Transport Management System (ITMS) which makes real-time dynamic decisions on traffic flows to accelerate traffic.

RETAIL

AIoT has the potential to enhance the retail shopping experience with digital augmentation. The same smart cameras we referenced earlier are being used to detect shoplifters. Walmart recently confirmed it has installed smart security cameras in over 1,000 stores.

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One of the big innovations for AIoT involves smart shopping carts. Grocery stores in both Canada and the United States are experimenting with high-tech shopping carts, including one from Caper which uses image recognition and built-in sensors to determine what a person puts into the shopping cart.

The potential for smart shopping carts is vast—these carts will be able to inform customers of deals and promotion, recommend products based on their buying decisions, enable them to view an itemized list of their current purchases, and incorporate indoor navigation to lead them to their desired items.

A smart shopping cart company called IMAGR recently raised $14 million in a pre-Series A funding round, pointing toward a bright future for smart shopping carts.

CONCLUSION

AIoT represents the intersection of AI, IoT, 5G, and big data. 5G enables the cloud processing power for IoT devices to employ AI algorithms to analyze big data to determine and enact action items. These technologies are all relatively young, and as they continue to grow, they will empower innovators to build a smarter future for our world.

GPT-3 Takes AI to the Next Level

“I am not a human. I am a robot. A thinking robot… I taught myself everything I know just by reading the internet, and now I can write this column. My brain is boiling with ideas!” – GPT-3

The excerpt above is from a recently published article in The Guardian article written entirely by artificial intelligence, powered by GPT-3: a powerful new language generator. Although OpenAI has yet to make it publicly available, GPT-3 has been making waves in the AI world.

WHAT IS GPT-3?

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Created by OpenAI, a research firm co-founded by Elon Musk, GPT-3 stands for Generative Pre-trained Transformer 3—it is the biggest artificial neural network in history. GPT-3 is a language prediction model that uses an algorithmic structure to take one piece of language as input and transform it into what it thinks will be the most useful linguistic output for the user.

For example, for The Guardian article, GPT-3 generated the text given an introduction and simple prompt: “Please write a short op-ed around 500 words. Keep the language simple and concise. Focus on why humans have nothing to fear from AI.” Given that input, it created eight separate responses, each with unique and interesting arguments. These responses were compiled by a human editor into a single, cohesive, compelling 1000 word article.

WHAT MAKES GPT-3 SPECIAL?

When GPT-3 receives text input, it scrolls the internet for potential answers. GPT-3 is an unsupervised learning system. The training data it used did not include any information on what is right or wrong. It determines the probability that its output will be what the user needs, based on the training text themselves.

When it gets the correct output, a “weight” is assigned to the algorithm process that provided the correct answers. These weights allow GPT-3 to learn what methods are most likely to come up with the correct response in the future. Although language prediction models have been around for years, GPT-3 can hold 175 billion weights in its memory, ten times more than its rival, designed by Nvidia. OpenAI invested $4.6 million into the computing time necessary to create and hone the algorithmic structure which feeds its decisions.

WHERE DID IT COME FROM?

GPT3 is the product of rapid innovation in the field of language models. Advances in the unsupervised learning field we previously covered contributed heavily to the evolution of language models. Additionally, AI scientist Yoshua Bengio and his team at Montreal’s Mila Institute for AI made a major advancement in 2015 when they discovered “attention”. The team realized that language models compress English-language sentences, and then decompress them using a vector of a fixed length. This rigid approach created a bottleneck, so their team devised a way for the neural net to flexibly compress words into vectors of different sizes and termed it “attention”.

Attention was a breakthrough that years later enabled Google scientists to create a language model program called the “Transformer,” which was the basis of GPT-1, the first iteration of GPT.

WHAT CAN IT DO?

OpenAI has yet to make GPT-3 publicly available, so use cases are limited to certain developers with access through an API. In the demo below, GPT-3 created an app similar to Instagram using a plug-in for the software tool Figma.

Latitude, a game design company, uses GPT-3 to improve its text-based adventure game: AI Dungeon. The game includes a complex decision tree to script different paths through the game. Latitude uses GPT-3 to dynamically change the state of gameplay based on the user’s typed actions.

LIMITATIONS

The hype behind GPT-3 has come with some backlash. In fact, even OpenAI co-founder Sam Altman tried to fan the flames on Twitter: “The GPT-3 hype is way too much. It’s impressive (thanks for the nice compliments!), but it still has serious weaknesses and sometimes makes very silly mistakes. AI is going to change the world, but GPT-3 is just a very early glimpse. We have a lot still to figure out.”

Some developers have pointed out that since it is pulling and synthesizing text it finds on the internet, it can come up with confirmation biases, as referenced in the tweet below:

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WHAT’S NEXT?

While OpenAI has not made GPT-3 public, it plans to turn the tool into a commercial product later in the year with a paid subscription to the AI via the cloud. As language models continue to evolve, the entry-level for businesses looking to leverage AI will become lower. We are sure to learn more about how GPT-3 can fuel innovation when OpenAI becomes more widely available later this year!

Everything You Need to Know About XRP and the Ripple Payment Network

While many cryptocurrencies aim to decentralize the banking system, one currency stands alone in their attempts to collaborate with banks: Ripple.

In our past two articles, we’ve spotlighted the top 10 cryptocurrencies to look out for in 2018 and the top trends to know about to invest wisely in cryptocurrency in 2018. Both articles had one common topic: Ripple.

While Bitcoin was created to decentralize the financial industry, Ripple is the only digital asset actively working with banks to improve rather than undermine their operations. Ripple boasts the ability to process on average over 1,500 transactions per second, making it the fastest cryptocurrency on the market. Ripple has teamed up with Western Union, Santander, American Express, and more to test the fastest cross-border transaction network available.

The process of making cross-border payments is unnecessarily tedious. In the internet era, the only reason why a currency transfer should take a week to process is because of  outdated procedures. Ripple attempts to create the currency exchange for the digital age. While traditional international transfers require two banks, two reserve banks, two correspondents, and up to a week to process, Ripple offers a transfer method that reduces the time and costs of traditional methods while also offering less failure points and higher security.

Check out Team KoinOK’s Medium post for a smooth summary of how Ripple changes the traditional transfer process.

The other major difference between Ripple and Bitcoin lies in their ledgers. While Bitcoin has a completely decentralized ledger enabled by proof-of-work, Ripple is owned by a private company. Ripple’s internal ledger does not use proof-of-work, but rather a consensus protocol with an amendment system that enacts all amendments that receive 80% support from developers over the course of two weeks. Ripple’s ledger is internal and therefore centralized.

Ripple consists of two components: the digital currency (XRP) and an open payment network that facilitates the transactions. Ripple markets the payment network toward banks as a way of enacting real-time settlements. Ripple is designed as a currency-agnostic transaction system. In order to avoid a currency exchange, currencies are converted into XRP and then sent to the recipient. Unlike Litecoin, XRP are not intended in the long run to be used by consumers to purchase products, but instead to be a middle-man currency that enables instant transactions. XRP and the Ripple network are designed to create a currency-agnostic value web designed to do for currency transfer what email did for messaging.

Rather than take our word for it, check out this awesome summary by Ripple CEO Brad Garlinghouse:

WHAT IS THE CONNECTION BETWEEN THE VALUE OF XRP AND THE PAYMENT NETWORK?

The acute investor must ask: if the Ripple payment network is Ripple’s main innovation, then what is the value of XRP? The long-term value of a cryptocurrency will be dictated by the problem that it is solving. If Ethereum becomes the platform for executing smart-contracts for a massive corporation like Amazon, then that ensure it’s existence in the long run, improving its function as a  store of value. If Ripple becomes the main transfer network for banks, its existence in the long term will be ensured and the function as a store of value will be greatly enhanced.

BOTTOM LINE

The transparency of the team behind the Ripple network and their vision of the platform instills great confidence in its ability to maintain value as a currency. If Ripple can achieve its goal of creating an internet of value where banks can exchange currency as easily as information, then it will definitely have the staying power to outlast the alt-coins and attain significant value over the coming years.