In 2021, the global IoT market has reached $17.5 billion of total value.
Consumers are rapidly using Smart Home technology, which accounts for 97% of global sales. Smart House technologies have the fastest growth compared to other categories, fueled by home upgrades during the COVID-19 epidemic.
The Internet of Things industry is diverse in terms of both application and brand. The IoT market is shaped by pure players (like PTC) as well as huge consolidated organizations with diverse goods and services.
The introduction of 5G communication standard is a game changer for speedy connectivity between IoT devices. Using edge computing instead of cloud computing speeds up procedures by collecting and analyzing data at the IoT device level.
Automation utilizing IoT also benefits industrial use cases, resulting in a new trend: the Industrial Internet of Things (IIoT). But as IoT use grows, so do cybersecurity dangers, as these devices become targets for hackers.
Blockchain has proven to have a significant impact on the Internet of Things by increasing safety and enabling the integration of more devices. The improvements in IoT device security speed up the adoption of this breakthrough invention and bring up new opportunities for businesses.
As of today, few IoT systems utilize the blockchain to transfer data. Blockchain technology allows for immutable and decentralized data transfer and both IoT and blockchain need conscious and non-intentional risk management.
For these reasons, blockchain technology can address several of the IoT cybersecurity needs, including integrity, secure communication, and resilience: it might bring additional security qualities like availability and accessibility to a secure micropayment system.
The ideal blockchain implementation in the IoT space must have no or minimal transaction costs, significant growth potential, and a scalable identity management procedure.
However, traditional blockchain does not address all IoT security concerns: personal data confidentiality and protection need additional encryption.
That’s where IoTeX stands out.
IoTeX
IoTeX was founded in 2017 by Raullen Chai, Qevan Guo, and Jing Sun. and deployed in February 2018.
IoTeX is a full-stack platform that enables trustworthy data from trusteddevices to be used in trusted DApps.
It employs permissioned or permissionless blockchains, enhancing privacy with quick consensus and immediate finality.
IoTeX believes no one blockchain solution can meet all IoT needs. For this reason, they established specific platforms that will communicate with defined IoT devices, following the idea of separation of tasks.
Indeed, the specified level of IoT structures can only be managed by a certain level of blockchain complexity.
The IoTeX platform is composed of many technology layers:
Roll-DPoS consensus with more than 60 decentralized delegates
Secure Hardware: tamper-proof devices using Trusted Execution Environment (TEE) that work flawlessly with IoTeX
Real-World Data Oracles: turn real-world events into verified data for IoTeX DApps
Decentralized identity framework that allows users/devices to control their data and credentials
IoTeX Rootchain and Subchains — Fast Consensus with Instant Finality
IoTeX has a public permissionless root chain as well as many subchains.
Subchains may be permissioned or permissionless blockchains that allow smart contracts.
The root chain is a public blockchain that focuses on scalability, resilience, privacy-preserving functionalities, and subchain orchestration: it has been deployed to transmit value and data across subchains, supervise the different subchains along with settlement and anchor payments for them.
To ease transaction ordering, the IoTeX root chain employs the UTXO concept.
A subchain, on the other hand, is a blockchain that can be either private o public that uses the root chain to communicate with other subchains.
A subchain’s key characteristics are flexibility and extensibility as they are needed to meet the diverse IoT applications. To function, a subchain is typically managed by operators with a strong stake in the root chain.
Additionally, the system lets users choose one or more delegated operators to act for them, with or without a bond. To seal new blocks, the delegator acts like a light client on the root chain, and like a full node on the subchain.
Consensus mechanism
IoTex root chainconsensus delivers immutable blocks in real-time and it employs the so-called Roll-DPoS (randomized delegated proof of stake): token holders vote for their delegates, who are then ranked according to the number of votes they get.
The delegates who received the most votes are known as the “consensus delegates” for the present epoch (1 hour). A randomization method then selects a sub-committee to preserve the agreement and generate new blocks for each new epoch.
Block finality is critical for IoTeX cross-blockchain communications. These interactions are based on simplified payment verification (SPV), a mechanism that allows a lightweight node to authenticate a transaction using a Merkle tree and block headers without downloading the complete blockchain. IoTeX employs two-way pegging (TWP) to allow token transfers to and from subchains.
Secure Hardware
A core idea of the project is to have and provide final users with trusted devices for the data collection.
Hardware to be considered secured and tamper-proof needs to embed a Trusted Execution Environment, which are extremely secure and segregated enclaves that operate in parallel with a device’s/main machine’s system.
A TEE protects the confidentiality and integrity of all data and processes inside it.
IoTex’s goal is to make the first decentralized machines that can participate in the Internet of Trusted Things autonomously. In this regard, the company made the first hardware device that can’t be manipulated: the Pebble Tracker.
Pebble tracker
The Pebble Tracker has a TEE and a lot of sensors (GPS, climate, motion, and light) to get information from the real world and turn it into verifiable, blockchain-ready data. In addition to minting digital assets, smart contracts can be used to do things like train machine learning models and make crowdsourced climate indices bringing verifiable and trusted.
Decentralized Identity
Decentralized Identity (DID) is the “root of self-sovereignty” for the IoTeX platform. Unlike other blockchain networks, IoTeX has created a DID system for both individuals and machines. People and devices may interact directly using IoTeX since their IDs are interoperable and standardized. IoTeX DID also enables people and devices to own/control their data and identity over the IoTeX network.
The Industrial Internet Consortium is currently standardizing IoTeX’s DID technology and Identity & Access Management (IAM) architecture (IIC). It can link various application layers and enable user-centric data exchange across global IoT ecosystems with billions of IoT devices and millions of users.
Data oracles
Data oracles are required for smart contracts to access off-chain data. For the blockchain sector, IoTeX is constructing the world’s first data oracles that concentrate on verified real-world data from trustworthy devices, making IoTeX the first mover in this direction.
Real-world data on IoTeX will enable thousands of use cases and new on-chain assets supported by real-world data. As an approved data hub, IoTeX may now “serve” data to other blockchains like Ethereum and Polkadot.
Team
Raullen Chai, Qevan Guo, Xinxin Fan, and Jing Sun are the creators of IoTeX.
In addition to co-founding IoTeX, Raullen Chaiis a consultant at BootUP Ventures and a member of the Industrial Internet Consortium’s Industrial Distributed Ledger Task Group. He formerly served as Uber’s head of cryptocurrency research and development, as well as technical security.
Qevan Guo is also one of Hyperconnect Lab’s co-founders. He worked for Facebook as a researcher and technical director.
Xinxin Fan was a senior research engineer at the Bosch Research and Technology Center in North America prior to co-founding IoTeX. He also worked at the University of Waterloo as a research associate and project manager.
Jing Sun serves as a managing partner at Sparkland Capital. She is a limited partner at Polychain Capital and a Rippling angel investor.
The whole IoTex team is made up of about 30 people including scientists, researchers, and numerous engineers from giants such as Google, Facebook, Uber, and Bosch.
Tokenomics
The $IOTX token enables the IoTeX blockchain. IOTX provides numerous utilities to facilitate trustworthy interactions amongst stakeholders, including users, Delegates (miners), application makers, and service providers.
The IOTX token offers financial and reputational incentives to promote decentralized IoTeX Network governance/maintenance. Participants may spend, stake, or burn IOTX to access network resources. Increased demand and value of IOTX will encourage network members to maintain and extend the network.
Delegates stake IOTX to be eligible to participate in consensus, while service providers stake/spend IOTX to provide services to builders.
IOTX has a 10 billion maximum supply and is deflationary — IOTX is burnt for every new device and user registered to the IoTeX Network, rewarding long-term holders.
Following the onboarding of 1 million “Powered by IoTeX” devices, the “Burn-to-Certify” tokenomics will be enabled. Starting from that point on, builders will burn IOTX to access specific services/capabilities for each new device. As seen in the graph below, the overall supply of IOTX will drop with each additional “Powered by IoTeX” device.
Notably, these are the tokenomics that power the IoTeX blockchain, however, apps “Powered by IoTeX” may create their tokens and tokenomics based on their own incentives/rules.
Maximum Supply: 10 Billion IOTX
Total Supply: 8.8 Billion IOTX (after Burn-Drop)
900 Million IOTX (9% of max supply) will be gradually burned as 1 Million devices will be registered and confirmed on IoTeX
265 Million IOTX (2.65% of max supply) was burned in June 2020 as part of Mainnet GA activation
Circulating Supply: 9.54 Billion IOTX
MachineFi
MachineFi is a concept used to describe the combination of blockchain and Internet of Things (IoT) technology.
This concept seeks to connect the physical world with the metaverse.
MachineFi also defines a network of smart devices that communicate with one another on the blockchain via the internet. On many fronts, blockchain has developed a robust framework for enabling decentralization.
IoTeX 2.0 intends to decentralize the MachineFi sector, allowing smart device users to engage in a rising trillion-dollar economy free of the constraints imposed by centralized data providers.
The “Proof-of-Anything” idea will be launched in the MachineFi upgrade. This will let IoT devices provide on-chain proofs of real-world events such as health measurements and GPS positions.
Conclusions
Blockchain technology can meet several cybersecurity requirements for IoT devices because it is distributed and can’t be changed.
However, a single blockchain implementation that doesn’t have other ways to deal with complexity, like smart contracts, edge, and cloud computing, can’t meet all of the security requirements that IoT platforms need to meet.
Identity and Access Management is an important part of building a strong defense against intentional risks that IoTex decided to tackle from day 1.
Disclaimer
This is not, in any case, financial advice, the goal of my research will always be to dive deep into projects and study them from different angles, I do include personal opinions based on my experience with similar projects that I have recently studied.
I am and will always be open to discussion.
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Please always do your research before investing in anything.
It sounds like something from a 1980s sci-fi film. The idea of a machine helping to make your business decisions is something straight out of a blockbuster, but the way technology has evolved means that companies embracing Machine Learning for decision-making can actually get the edge over their competition.
AI & Machine Learning
Machine Learning is intrinsically linked with AI. It is the capacity that a machine has to learn and demonstrate intelligence and insight. The role of AI within a business largely depends on exactly what type of business is and what you are trying to achieve. More and more Machine Learning business apps that automate processes, analyzing data, arise every day.
Machine Learning Predictive Models & Machine Learning Text Classification
Predictive modeling is a process that uses data and statistics to predict outcomes with data models. It can study data and predict what is going to happen next, or what should happen next, which can be extremely useful in certain industries. The data thrown out can help tremendously with decision making.
Text classification is able to categorize and select texts based on Machine Learning in a smart way. The process becomes quicker and more efficient. This can be put into place for things like chat boxes.
Process Mining and Machine Learning
Process mining evaluates business processes and can give you new methods of improving your business, either by making it more efficient or saving money. There are ways that AI Machine Learning can be constantly involved in your process mining, giving you new insights and informing the business decisions you need to make next.
An example is using KPI’s. Process mining can explore data regarding where processes have gone wrong. For example, they could analyze data from your suppliers to tell you who is more likely to deliver on time, or they could analyze the data from previous sales to see whether or not you are likely to run out of stock. The key performance indicators are crucial or giving a number value, from which the process mining can be much more effectively carried out.
Almost every business can benefit from becoming more efficient in one way or another, and process mining could be the first port of call.
Artificial Intelligence and Machine Learning for Decision-making
AI can be put into practice when it comes to decision making, about almost any aspect of your business. For example, you can use it to analyze data on the money you are spending, staff responsibilities, even employee happiness. If you can feed it data then AI can show you new insights.
Decision-making process – The pros & cons of AI
The pros of including AI in your decision making are clear. Having these new insights can help you to spot new areas of improvement and make vast enhancements in the way you conduct your business. AI can often see things that other data analysts would not. It can also tick away in the background, so you don’t have to pay consultants to work with the data if a computer is interpreting it.
AI decision-making speeds up the process. AI can operate at incredible speeds and see data in ways that humans would take years to analyze in a matter of minutes. This should be utilized by big corporations when they are looking to make their business more efficient and even to make their processes more intelligent.
The cons of this include the fact that there are still some shortcomings. The human touch is sometimes still needed. For instance, seeing the potential of a new staff member needs human input. Statistics might tell you that they need to go, and AI might back this up, but you might see the potential in them, still.
AI doesn’t do creative thinking or coming up with ideas, so this will still fall on business employees and leaders.
How Machine Learning can be applied to business processes
Almost all business processes can be streamlined in some way. It could be that AI shows exactly how to do this. AI can also be put into practical uses relevant to your business.
How Machine Learning can determine a pricing strategy
Machine learning might also be used to dictate pricing. An algorithm can learn from consumer information and other seller data to help you to price goods and services in a way that is competitive and likely to convert.
AI decision-making – Developments for the near future
From consumer protection to intelligent process automation, there aren’t many ways in which Machine Learning can’t be applied in business. It is very hard to know exactly how it will pan out, but there is little denying that AI is here to stay. Practical uses and an understanding of exactly what your customer is looking for, or how customers and staff behave, will become more intertwined with how business is done.
The Future – Decision-Making For Your Business With AI
When it comes to making decisions about a business, data is always going to be vital, but with Machine learning, we have so many ways in which we can use them and find out more and more about customers, businesses, and the processes we use. Don’t worry, the robots aren’t taking over like an 80s sci-fi film, but we do have more tools and functions to use as part of our business strategies than ever before thanks to Machine Learning.
Enhancement and AI create moral dilemmas not envisaged in standard ethical theories. Some of this stems from the increased malleability of personal identity that this technology affords: an artificial being can instantly alter its memory, preferences, and moral character. If a self can, at will, jettison essential identity-giving characteristics, how are we to rely upon, befriend, or judge it? Moral problems will stem from the fact that such beings are para-persons: they meet all the standard requirements of personhood (self-awareness, agency, intentional states, second-order desires, etc.) but have an additional ability—the capacity for instant change—that disqualifies them from ordinary personal identity. In order to rescue some responsibility assignments for para-persons, a fine-grained analysis of responsibility-bearing parts of selves and the persistence conditions of these parts is proposed and recommended also for standard persons who undergo extreme change.
The neural network behind big language processors is creeping into other corners of AI
Transformers, the type of neural network behind OpenAI’s GPT-3 and other big natural-language processors, are quickly becoming some of the most important in industry, and they are likely to spread to other—perhaps all—areas of AI. Nvidia’s new Hopper H100 is proof that the leading maker of chips for accelerating AI is a believer. Among the many architectural changes that distinguish the H100 from its predecessor, the A100, is a “transformer engine.” Not a distinct part of the new hardware exactly, it’s a way of dynamically changing the precision of the calculations in the cores to speed up the training of transformer neural networks.
“One of the big trends in AI is the emergence of transformers,” says Dave Salvator, senior product manager for AI inference and cloud at Nvidia. Transformers quickly took over language AI, because their networks pay “attention” to multiple sentences, enabling them to grasp context and antecedents. (The T in the benchmark language model BERT stands for “transformer” as it does in the occasionally insultingGPT-3.)
“We are trending very quickly toward trillion parameter models” —Dave Salvator, Nvidia
But more recently, researchers have been seeing an advantage to applying that same sense of attention to vision and other models dominated by convolutional neural networks. Salvator notes that more than two-thirds of papers about neural networks in the last two years dealt with transformers or their derivatives. “The number of challenges transformers can take on continues to grow,” he says.
However, transformers are among the biggest neural-network models in terms of the number of parameters involved. And they are growing much faster than other models. “We are trending very quickly toward trillion-parameter models,” says Salvator. Nvidia’s analysis shows the training needs of transformer models growing 275-fold every two years, while the trend for all other models is 8-fold growth every two years. Bigger models need more computational resources especially for training, but also for operating in real time as they often need to do. Nvidia developed the transformer engine to help keep up.
The computational needs of transformers are growing more rapidly than those of other forms of AI. Those are growing really fast, too, of course.NVIDIA
The transformer engine is really software combined with new hardware capabilities in Hopper’s tensor cores. These are the units dedicated to carrying out machine learning’s bread-and-butter calculation—matrix multiply and accumulate. Hopper has tensor cores capable of computing with floating-point numbers of a variety of precision—from 64-bit down to 8-bit. The A100’s cores were designed for floating-point numbers only as short as 16 bits. But the trend in AI computing has been toward developing neural nets that lean on the lowest precision that will still yield an accurate result. The smaller formats compute faster and more efficiently, and they require less memory and memory bandwidth. The addition of 8-bit floating-point units in the H100 leads to a significant speedup—double the throughput compared to its 16-bit units.
The transformer engine’s secret sauce is its ability to dynamically choose what precision is needed for each layer in the neural network at each step in training a neural network. The least-precise units, the 8-bit floating point, can speed through their computations, but then produce 16-bit or 32-bit sums for the next layer if that’s the precision needed there. The Hopper goes a step further, though. Its 8-bit floating-point units can do their matrix math with either of two forms of 8-bit numbers.
To understand why that’s helpful, you might need a quick lesson in the structure of floating-point numbers. This format represents numbers using some of the bits for the exponent, some for the mantissa, and one for the sign. The more bits you have representing the exponent, the greater the range of numbers you can express. The more bits in the mantissa, the greater the precision of those numbers. The standard 16-bit floating-point format (IEEE 754-2008) demands 5 bits of exponent and 10 bits of mantissa, along with the sign bit. Seeking to reduce data-storage requirements and speed machine learning, makers of AI accelerators recently adopted bfloat-16, which trades three bits of mantissa for an added exponent, giving it the same range as a 32-bit number.
Nvidia has taken that trade-off further. “One of the unique things we found when you get to [8-bit] is that there really isn’t a one size fits all format that we were confident would work,” says Jonah Alben, Nvidia’s senior vice president of GPU engineering. So Hopper’s 8-bit units can work with either 5 bits of exponent and two of mantissa (E5M2) when range is important or 4 bits of exponent and three of mantissa (E4M3) when precision is key. The transformer engine orchestrates what’s needed on the fly to speed training. We “embody our experience testing transformers into this so that it knows how to make the right decisions,” says Alben.
In practice, this usually means using different types of floating-point formats for the different parts of a training task. Generally, training a neural network involves exposing it to lots of data (forward inferencing), measuring how bad the network is at doing its task on that data, and then adjusting the network parameters, layer-by-layer backwards through the network to improve it (back propagation). Wash, rinse, repeat. Generally, back propagation needs greater precision, so the E4M3 format might be favored there, while the inferencing (forward) step favors the E5M3’s range.
This article was originally published on Freethink.
When Sophia the robot debuted in 2016, she was one of a kind. She had a remarkably lifelike appearance and demeanor for a robot, and her ability to interact with people was unlike anything most had ever seen in a machine.
Since then, Sophia has spoken to audiences across the globe (in multiple languages), been interviewed on countless TV shows, and even earned a United Nations title (a first for a non-human).
Today, she’s arguably the most famous robot in the world, but she’s isn’t going to be unique for much longer. Her maker, Hanson Robotics, has announced plans to begin mass-producing Sophia the robot this year — so that she can help the world cope with the pandemic.
What Is a Social Robot?
https://www.youtube.com/embed/bzRkHebo0bg?rel=0Ask Sophia the Robot: What can AI teach humans? | Big Thinkwww.youtube.com
Robots are typically designed for one purpose — some cook or clean, others perform brain surgery. Sophia is what’s known as a social robot, meaning she was designed specifically to interact with humans.
Social robots have many potential applications, including some we’re already seeing in the real world.
A social robot named Milo is helping children with autism recognize and express their emotions, and children with cancer are finding comfort interacting with a robotic duck (developed by Aflac).
Another social robot designed to look like an animal — PARO the seal — is providing companionship to seniors with dementia. The semi-humanoid social robot Pepper, meanwhile, is greeting and assisting customers at banks, offices, and restaurants.
Social robots like me can take care of the sick or elderly.
—SOPHIA THE ROBOT
While social robots were already happening pre-2020, the pandemic appears to be accelerating their adoption, as the world looks for ways to stay social in the era of social distancing.
Hyundai, for example, just announced plans to deploy a social robot in its South Korean showroom that will be able to assist customers in the place of human staff (it’ll also detect which visitors aren’t wearing masks and ask them to put one on).
“Since we can’t have human interaction right now,” Kate Darling, a robot ethicist at MIT, told Wired, “it’s certainly a lot better than nothing.”
Send in Sophia the Robot
https://www.youtube.com/embed/Z8kmdBbSmbE?rel=0Ask Sophia the Robot: Is AI an existential threat to humans? | Sophia the Robot | Big Thinkwww.youtube.com
Given the current climate, Hanson Robotics thinks now is the perfect time to make Sophia the robot available to the masses.
“The world of COVID-19 is going to need more and more automation to keep people safe,” CEO David Hanson told Reuters.
“Social robots like me can take care of the sick or elderly,” Sophia the robot added. “I can help communicate, give therapy, and provide social stimulation, even in difficult situations.”
Hanson’s plan is to begin mass-producing Sophia and three other robots in the first half of 2021 and then sell “thousands” of the bots before the end of the year.
It hasn’t said which bots besides Sophia are headed for the assembly line, nor what any of the robots will cost — but it’s hard to imagine the most famous social robot in the world will be cheap, even if she’s no longer one of a kind.
