Artificial Intelligence (AI) has a market full of hype, with vendors, customers, and media speaking non stop about the abilities of AI on worldwide and their contributions individually.
Blockchain is also generally hyped in the market, with technology providers and clients claiming all sorts of abilities that may or may not be possible. Combining AI and blockchain can obtain double the hype? On the other hand, AI is implemented real, the actual value in many endless ways we talk about every day.
Likewise, blockchain is starting to show value across a variety of applications and businesses. So, perhaps combining AI and blockchain will also show twice the power combined.
Also Read: How the blend of Blockchain & AI change the face of businesses?
The role of blockchain in AI
Blockchain is a decentralized, spread entries of transactions that have elements of clarity, trust, verifiability, and something called smart deals. Decentralized and assigned means that information that is stored across the network in such a way that each endpoint has access to the data without requiring access to a central server.
The network is also distributed because the transactions happen at each endpoint without requiring centralized coordination. An entry is a record of transactions. Blockchain records a ledger of interactions between two separate individuals whether it be a financial exchange or even a chain of custody showing when things have changed hands over time.
Since every block in the blockchain contains a different piece of information that is encrypted or encoded, the blockchain can help guarantee the trust and verifiability of data. The concept of the chain and block in the blockchain is that each block has its information and that information contains a link to the block before it, which develops the chain and provides a verifiable chain of custody.
No individual actor can change the information in a block without invalidating the entire chain of information in a particular block and thus messing up the chain. Since the chain is distributed in a myriad of other places and between other parties, to make changes to the chain, a consensus of all the parties would need to agree to the changes being made.
So how can blockchain help with AI? The first benefit is that you can share machine learning models among all parties without an intermediary. A good example is with facial recognition software. If one device knows what a person looks like and uploads it to the chain, other devices hooked up also know what that person looks like.
When other devices upload their facial recognition data, the other devices will gain the ability to use that for the facial recognition model. Since this happens on the blockchain, there is no central control over facial recognition, and as such, no one company owns or stores the data. This approach also allows for everyone to learn faster
AI systems can also use blockchain to facilitate the sharing of data used across multiple models. A great example is the use of machine learning models for product recommendations in online retail. If an online store knows the preferences of one shopper, and then that customer goes to a different store website, these two sites can be connected through a blockchain to share trusted personalization information.
This could potentially become a place of competition for smaller e-commerce sites that want to share personalization information. Instead of each company gathering their personalization data, they can share it amongst each other in a blockchain, providing competition to sites like Amazon and Walmart who have already developed their data systems to gather this information about their customers.
The benefit of this to the customer is that in exchange for sharing their information, they can get everything from better pricing to customized shopping experiences. This could also prevent data breaches if all information and payment systems are stored and shared through a blockchain rather than a centralized data server.
Also Read: Blockchain App Development
How blockchain can benefit AI
Not only can blockchains be used to share models and data, but blockchains can help serve a role as a “master brain” in a manner shared across multiple AI systems. If we can put all of these shared learning benefits and blockchain and AI together, the possibility may also be there to combine all of these things that can learn from their surroundings and then share that learning with all the AI systems on the network.
A major benefit would be that no one owns it and there’s no government control over the bot or the shared brain. It could potentially be unbiased because of the sheer amount of information coming in from different areas and different angles. Another application is addressing the challenge of explainable AI. One of the more significant problems with deep learning is that there isn’t a clear idea about what inputs result in what output and how that affected the whole sequence.
If something goes wrong in a deep learning neural network, we don’t have a clear idea of how to identify the problem and correct it. This is the problem of neural networks in effect being a “black box” without any real transparency or explainability. However, if we use blockchains, we can record how individual actions result in a final decision in a non-reputable manner, which allows us to go back and see where things went wrong and then fix the problem.
The blockchain would be used to record events, such as autonomous vehicle decisions and actions in a way that will not be modified later. This can also increase trust since the blockchain element is unbiased and is just for storage and analysis, anyone can go in and see what has happened. Finally, AI systems can be used to improve blockchains in general. Machine learning systems can keep an eye on what is happening in the blockchain. It can look for patterns and anomalies in the types of data being stored and actions being performed on a particular server and be used to alert users when something may be happening.
The systems can look for normal behavior and flag what seems to be unusual. The AI systems can help keep blockchain more secure, more reliable, and more efficient. While the worlds of AI and blockchain may be full of hype, there are actual, tangible, realistic ways in which the two emerging technologies can be used in ways that benefit each other and provide real outcomes for those looking to implement the technologies today in their environments.
In 2016, the calculable worth additional by the agriculture business was calculable at but one percent people GDP.
Factors like global climate change, increase and food security considerations have pushed the business to hunt a lot of innovative approaches to safeguard and improve crop yields.
As a result, AI is bit by bit evolving as a part of the technology evolution of the business.In this article we have a tendency to explore the applications of computer science to know current and rising trends for business leaders, and gift representative samples of common applications.Artificial Intelligence within the Agricultural business - Insights Before:Based on our analysis, the foremost common applications of AI in agriculture make up 3 main categories:Agricultural robots: - corporation’s area unit developing autonomous robots to perform the required agricultural tasks, like harvest crops and fast quicker than human labor.
Crop and Soil observation: - corporation’s area unit developing laptop vision and deep learning algorithms to method knowledge compiled by drones and / or software-based technology to watch crop and soil health.Predictive Analytics: - Machine learning models area unit being developed to find out and predict numerous environmental impacts on crop yields like global climate change.Blue watercourse Technology - Weed management:The ability to regulate weeds may be a high priority for farmers associate degreed an in progress challenge as weed killer resistance becomes a lot of common.
per a quest study conducted by the Weed Science Society of America on the impact of uncontrolled weeds on corn and soybean crops, annual losses to farmers area unit calculable at $ forty three billion.
Companies’ area unit exploitation automation and artificial intelligence to assist farmers realize a lot of economical ways in which to safeguard their crops from weeds.
The future of retail looks dire, as more brick-and-mortar stores close their doors.
U.S. retailers have announced 8,558 store closures so far this year, with total U.S. store closures reaching 12,000 by the end of 2019, Cordite Research reported Friday.While the Internet and automation are usually responsible for these closures, the same technology could be a solution for physical store locations, said Paul Winsor, general manager at Data Robot.“If retailers want to stay open at the current stores they operate in, my recommendation to them is: Do they understand the changing habits of those customers, and how are they shopping with them, in those places?” Said Winsor.“To survive in a tough, tough retail market, you have to start your business and make predictions based on learning from your historical data,” he says.
Technology is around to help companies understand their business from a data perspective,” says Winsor.
“Data is not as personal and accurate as machine learning can help you.”To make predictions in the past, retailers look at daily and weekly transaction data and draw conclusions from it, Winsor said.As technology has evolved and convenience has become a priority, online stores have become the primary way to shop.
If retailers refuse to grow and adapt to emerging retail infrastructure, they will inevitably fall behind.AI helps retailers in three ways“With AI, we are dealing with machines that can simulate intelligent behavior or simulate intelligent human behavior, that is, sense, reason, action, and adaptation,” said Altimeter lead analyst Brian Solis.
“One of the most popular ways that leading brands use AI today is machine learning.”“The difference is that with machine learning, systems can differentiate models from pure data sets, and with the right management, learn from those data to predict and predict results and improve performance over time,” Solis says.
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