You are reading the article Solana (Sol) Predicted To Seek Ai Solutions From(Fet) And Avorak Ai (Avrk) updated in December 2023 on the website Moimoishop.com. We hope that the information we have shared is helpful to you. If you find the content interesting and meaningful, please share it with your friends and continue to follow and support us for the latest updates. Suggested January 2024 Solana (Sol) Predicted To Seek Ai Solutions From(Fet) And Avorak Ai (Avrk)
As one of the fastest-growing blockchain ecosystems, Solana is constantly looking for ways to improve its performance and scalability. Recently, some members of the crypto community predicted that Solana (SOL) could seek AI solutions from chúng tôi (FET) and Avorak AI (AVRK)
Solana newsSolana has been making headlines in the crypto space due to its rapid growth in price and popularity. The platform’s fast transaction speeds and low fees have attracted many users and developers. With the growing demand for Solana’s services, there has been news suggesting that the PoS platform is looking to leverage AI technology to enhance its capabilities further.
What does Avorak AI (AVRK) offer?Avorak AI is a new AI crypto platform on the BNB Smart Chain. The platform leverages AI and blockchain technologies to offer businesses and individuals a comprehensive set of user-defined AI solutions. These AI-driven products and services will be paid for using the AVRK token.
There are several ways in which Avorak AI can enhance the Solana ecosystem. Through its AI-backed security monitors, Avorak can analyze and process vast amounts of data quickly and accurately within the Solana ecosystem, identifying and addressing or helping to address any potential issues or bottlenecks. Additionally, by leveraging Avorak’s machine learning algorithms, Solana can continuously improve its consensus mechanism, making it more efficient and scalable. By using natural language processing and other AI technologies, Avorak AI is developing chatbots and digital assistants, which Solana can use to help dApp developers easily create and deploy more sophisticated applications on its platform.
Avorak’s trading bot can also be helpful to Solana (SOL) traders and investors. The AI trade bot uses AI mechanisms, such as deep learning, to predict future prices of different crypto assets. It also provides visuals through its large sets of indicators and integration of services like TradingView. The Avorak Trade bot can be programmed to conduct automated trades on different exchanges using a simple non-code command-line input.
AVRK is currently trading at $0.180 in phase 4 of Avorak’s ICO. With Azbit planning to list Avorak AI (AVRK) once it’s launched and several other exchanges expected to follow, many analysts predict that the AI crypto might witness a significant price hike in July.
Fetch.ai price predictionSome AI crypto enthusiasts suggest that Solana might also look to chúng tôi to enhance its network capabilities. chúng tôi is a decentralized AI platform that enables autonomous agents to perform complex tasks without human intervention. The potential partnership between Solana (SOL) and chúng tôi (FET) can bring new innovations and dApps to the blockchain. With this collaboration, chúng tôi (FET) may see significant price appreciation as more people become aware of the capabilities of its AI crypto technology.
ConclusionBy leveraging AI technology, Solana can further enhance its network performance and scalability, making it an even more attractive blockchain. As the adoption of AI and blockchain technology continues to grow, partnerships like these are likely to become more common, bringing new innovations to the crypto industry. The potential benefits of these partnerships could be significant, not just for Solana but for the entire blockchain ecosystem.
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How To Prevent Ai From Creating Deepfakes?
Deepfakes are becoming increasingly sophisticated, and it is becoming more challenging to prevent AI from creating them. However, there are several steps that individuals and organizations can take to minimize the risk of deepfakes. In this article, we will explore various strategies and techniques to Prevent AI from Creating Deepfakes and safeguard the authenticity of digital media.
Also read: Best Deepfake voice generator free 2023
Developing strong security procedures is essential to safeguard against deepfakes. One effective approach is to implement a multistep authentication process that includes verbal and internal approval systems. By requiring multiple layers of verification, it becomes harder for malicious actors to bypass security measures. Additionally, organizations should consider changing communication channels for different types of information to reduce the risk of unauthorized access.
Using digital artifacts is an innovative technique to counter deepfakes. Digital artifacts are specially designed elements inserted into videos to disrupt the patterns of pixels that face detection software relies on. These artifacts introduce visual disturbances that degrade the quality of deepfakes, making it more challenging for AI algorithms to create convincing forgeries. This approach slows down the deepfake generation process and decreases the likelihood of successful manipulation.
Also read: What is DeepSwap AI? Use, Advantages & Alternatives
Leveraging AI-based tools to train computers in detecting deepfakes is a proactive defense strategy. These tools utilize machine learning algorithms to analyze visual cues and identify signs of forgery. By feeding the algorithms with a vast dataset of authentic and manipulated media, they learn to distinguish between genuine content and deepfakes. Furthermore, automated tools can compare digital artifacts stored in archives by different organizations, enabling the tracking of changes over time and aiding in the detection of deepfake tampering.
Maintaining better historical archives is crucial to protect against deepfake tampering with historical records or important documentation. By establishing comprehensive archives with proper security measures, organizations can preserve the integrity of valuable information. This includes implementing protocols for access control, encryption, and regular backups to prevent unauthorized modifications or deletions. Historical archives serve as a reference point for comparison and verification, helping to identify potential deepfake attempts.
In combating deepfakes, involving human moderators can be highly effective. Human judgment and intuition play a significant role in detecting and removing deepfakes that slip past automated systems. Moderators with expertise in media analysis and digital forensics can identify subtle visual discrepancies, inconsistencies, or anomalies that AI algorithms might overlook. Collaborating with moderators also enables continuous monitoring and timely response to emerging deepfake threats.
Implementing good basic security procedures is remarkably efficient at countering deepfakes. For instance, having automatic checks and safeguards in place can help identify and flag suspicious activities or content. Regularly updating software and operating systems, utilizing secure networks, and educating users about potential risks are all essential components of a robust security infrastructure. By adopting these best practices, individuals and organizations can reduce vulnerabilities and enhance their resilience against deepfake attacks.
Also read: Transform Your Look with Face Swapping Apps
A: Digital artifacts are designed to disrupt the patterns of pixels that face detection software relies on. By inserting these artifacts into videos, the quality of deepfakes is compromised, making it harder for AI algorithms to create convincing forgeries. The presence of digital artifacts introduces visual disturbances that slow down the deepfake generation process and reduce the chances of successful manipulation.
A: Yes, automated tools that utilize AI algorithms can be effective in detecting deepfakes. By training these tools with authentic and manipulated media, they learn to analyze visual cues and identify signs of forgery. Additionally, automated tools can compare digital artifacts archived by different organizations, enabling the tracking of changes over time and aiding in the detection of deepfake tampering.
A: Maintaining better historical archives serves as a reference point for comparison and verification. By preserving valuable information in secure archives with proper access controls and encryption, organizations can protect against deepfake tampering. Historical archives provide a trusted source for verifying authenticity and identifying potential deepfake attempts.
A: Human moderators with expertise in media analysis and digital forensics play a vital role in detecting and removing deepfakes. Their knowledge and intuition enable them to identify subtle visual discrepancies or anomalies that AI algorithms might miss. Collaborating with moderators allows for continuous monitoring and timely response to emerging deepfake threats.
A: While good security procedures are essential in countering deepfakes, they should be used in conjunction with other preventive measures. Good security procedures, such as regular software updates, secure networks, and user education, enhance an organization’s resilience against deepfake attacks. However, a comprehensive approach that combines multiple strategies provides a more robust defense against the evolving threat landscape.
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The Industries Set To Benefit From The Ai Boom
Artificial intelligence (AI) was once one of those things that was reserved for geeks or sci-fi fans. Things have certainly changed and the whole concept of AI has become mainstream. No matter where you turn there is talk about the most recent developments and where people see the future heading.
In part, we have the likes of ChatGPT to thank for the rise of AI. Not for the technology itself but more for the increased awareness of what it can offer. There are certain industries that are already benefiting from what AI has to offer, and they’re likely to benefit hugely in the future. Let’s take a look at just some of these industries now.
Gambling industryThe gambling industry has long been at the forefront when it comes to technological developments. The rise of online casinos alone goes to demonstrate this, but we also have the integration of virtual reality too. This means that this industry is primed to tap into what AI has to offer.
To some extent, casinos are already doing this with the likes of chatbots and know-your-customer checks. However, there is so much more to come. AI has the potential to massively benefit the speed of fast withdrawal processes at online casinos, as well as improving overall security.
HealthcareDeveloping AI lends itself perfectly to the healthcare industry. When you consider one of the major roles of healthcare providers, it becomes clear why. Any provider has the need to collect masses of data that are both accurate and relevant. AI can ensure that this process happens with ease.
It is also more than possible that AI will go on so that it can assist with predictive healthcare. The power of AI means that it will be able to carry out predictive analyses that are highly accurate and this will assist doctors when it comes to managing patient needs.
Another significant area where AI will be useful in healthcare is that of scans. With the ability to analyse images, AI will make it much easier, and faster, for diagnoses to be given.
Banking and financial servicesAI and the financial sector just naturally go hand in hand. Just like the healthcare industry, this is one that deals with an abundance of data. This needs collating and organising. Something that AI can do with ease. AI is already being used in the banking industry to some extent. Primarily, it serves to identify potentially fraudulent transactions.
AI can also be used in the lending process. By analysing credit history, AI will be able to report to banks and tell them the probability of someone meeting repayments. This will lead to the process being streamlined, which is good for the customer, and will reduce the chances of bad debt, which is great for the banks.
LogisticsThis is an industry where AI is set to have a seriously dramatic impact. This is down to how it is able to carry out predictive analysis with ease. In terms of logistics, this helps by predicting the need for inventory, as well as minimising routes to keep overhead costs down.
RetailIn the retail sector, the use of AI is nothing new. It is already being used to optimise supply chains as well as effectively manage stock levels. It even allows retailers to understand more about how customers enter and shop a store so that the layout can be optimised to encourage a bigger spend.
Beyond this, we also have self-shop stores to come. The likes of Amazon have already proven that this can work and, as the technology develops, you can be sure that other retailers will be on board with this too.
AI is also extremely useful when it comes to online retail. It allows companies to suggest other products and maximise sales.
CybersecurityThe majority of cybersecurity companies operate by referring to large databases that are able to check for virus attacks. AI can work alongside such databases with ease. As the tech is being developed, the hope is that it will offer a proactive approach to security by dealing with potential issues before they take off.
The way in which AI works means that it can be used as a set-and-forget solution. It will have the ability to continually update itself so that it knows the type of attacks that are possible. It will then be able to automatically create solutions that will block these.
MarketingAI is a marketer’s dream. It has the potential to assist in this industry in two major ways. The first of these is that it will allow for messages that are more personalised. Secondly, it will assist with more accurate targeting.
TransportationOne of the biggest developments in the world of AI is that of autonomous vehicles. Self-driving cars are already a reality and their mainstream adoption is only a matter of time. Companies such as Uber are already exploring how this tech can be used within its business.
What Is Artificial Intelligence(Ai) And How Ai Will Transform Cybersecurity
Considering that the electronic revolution started, there have been many instances of information breaches, identity theft, and lack of cash. Cyber-attacks are now very pervasive and indiscriminate, because they may affect any person, organization, business, or body. Therefore, most of us must comprehend the expanding requirement for cybersecurity.
Fortunately, there also have been engineering improvements with significant effects on cybersecurity. Artificial Intelligence (AI) is just one of those techniques and tools which are significant game-changers within the area of cybersecurity. In this guide, we investigate the present and prospective impact of AI on cybersecurity.
Related: – Future of Artificial Intelligence for 2023
What Is Artificial Intelligence?AI is a general expression that spans several capacities. In its heart, Artificial Intelligence intends to imitate human intelligence to make decisions and resolve problems. Usually, people pre-code a particular set of controls that enables a machine to perform a job. The machine only depends on this particular code to create results, and it’ll create the exact same effect no matter how frequently you run the code.
Related: – What You should know for a career in Artificial Intelligence
Current Impact of AI and CybersecurityThere are a variety of methods by that Artificial Intelligence is making a difference so far as cybersecurity is worried. They comprise:
1. Cyber Threat DetectionOrganizations must have the ability to discover cyber-attacks well beforehand in order to foil whatever the cybercriminals might be trying to achieve. AI has turned out to be tremendously helpful in regards to cyber hazard detection.
According to Forbes, 61 percent of businesses attest that it’s impossible for them to detect breach efforts without the assistance of AI technology. AI systems may identify unusual patterns, including excessive use of resources (CPU, memory, etc.), strange transfers of information, strange connections, incorrect logins, and odd visitors, etc..
2. Vulnerability Management And Prevention ControlAfter identifying potential dangers, AI systems instantly categorize them under various degrees of seriousness, i.e., low, moderate, large, or crucial. What is notable about AI is that it may detect and monitor thousands of phishing resources and remediate much faster than individuals could.
AI methods play an active part in vulnerability prevention and management control. It may remove the cyber dangers that it defines by dropping packets, blocking IP addresses, and shutting down procedures, etc..
3. Password Protection And AuthenticationAs per a Pew Research Report, roughly 24% of internet users keep their passwords at an electronic document or notice on one of the apparatus. When this permits users to get their login information easily, it provides cybercriminals a simple time getting a record of these details and obtaining their account.
Another area that AI has been influenced is that the area of biometrics, that’s the science of verifying the individuality. AI biometrics provides a solution by giving validation for features that are tough to mimic.
AI biometrics may be used for authentication by assessing two kinds of characteristics to recognize an individual: physical and behavioral. Behavioral characteristics derive from identifying behaviors such as the tone of your voice, your typing mode, and your error prices. Physical features derive from quantifiable and distinctive characteristics like your own face, the iris of your eye, fingerprints, or DNA.
The fund market is just one of those businesses which are undergoing the positive effect of AI biometrics. This technology is gaining momentum now we have many banking, fund programs, and ATMs necessitating facial or voice recognition.
Negative Implications Of AI On CybersecurityAs we take notice of the truly important capacities of AI, we must also recognize that additional progress in AI can contribute to new kinds of cyber threats. For example, cybercriminals may use AI to hack systems much faster and efficiently than individuals can. This is only one of those reasons why cybersecurity is significant.
The principal implications of AI into the cybersecurity landscape comprise the amplification of present dangers, the variant of the character of present risks, and the evolution of new threats. The behavior-modeling procedures of Artificial Intelligence, consequently, ought to be constant to stop AI from getting outdated or obsolete.
Future Impact Of AI And CybersecurityAI technology will continue to have a significant effect on several businesses around the globe. Since AI technology is still integrated into the devices we use daily, AI’s participation within our daily lifestyles will continue growing.
For cybersecurity, the principal focus is to discover ways in which this technology provides faster analysis and reduction of cyber threats. It will get even better at providing effective answers to cyber-attacks, proactively replicating the very best defense mechanisms made by human analysts.
What Ai Governance Can Learn From Crypto’s Decentralization Ethos?
Know why there is a need for more transparency and accountability of artificial intelligence and AI governance
The development, application, and capabilities of AI-based systems are evolving rapidly, leaving largely unanswered a broad range of important short and long-term questions related to the social impact, governance, and ethical implementations of these technologies and practices. In this article, we will discuss what AI governance can learn from Crypto’s Decentralization Ethos and why there is a need for governance.
Many sectors of society rapidly adopt digital technologies and big data, resulting in the quiet and often seamless integration of AI, autonomous systems, and algorithmic decision-making into billions of human lives. AI and algorithmic systems already guide a vast array of decisions in both private and public sectors. For example, private global platforms, such as Google and Facebook, use AI-based filtering algorithms to control access to information. AI can use this data for manipulation, biases, social discrimination, and property rights.
Humans are unable to understand, explain or predict AI’s inner workings. This is a cause for rising concern in situations where AI is trusted to make important decisions that affect our lives. This calls for more transparency and accountability of Artificial Intelligence and the need for AI governance.
Lessons from Crypto’s Decentralization EthosThe titans of U.S. tech have rapidly gone from being labeled by their critics as self-serving techno-utopianists to being the most vocal propagators of a techno-dystopian narrative.
This week, a letter signed by more than 350 people, including Microsoft founder Bill Gates, OpenAI CEO Sam Altman, and former Google scientist Geoffrey Hinton (sometimes called the “Godfather of AI”) delivered a single, declarative sentence: “Mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war.”
According to Coindeck, two months ago, an earlier open letter signed by Tesla and Twitter CEO Elon Musk along with 31,800 others, called for a six-month pause in AI development to allow society to determine its risks to humanity. In an op-ed for TIME that same week, Eliezer Yudkowsky, considered the founder of the field of artificial general intelligence (AGI), said he refused to sign that letter because it didn’t go far enough. Instead, he called for a militarily-enforced shutdown of AI development labs lest a sentient digital being arises that kills every one of us.
Why AI Governance is Important?Job Threats– Automation has been eating away at manufacturing jobs for decades. AI has accelerated this process dramatically and propagated it to other domains previously imagined to remain indefinitely in the monopoly of human intelligence. From driving trucks to writing news and performing recruitment tasks, AI algorithms are threatening middle-class jobs like never before. They might set their eyes on other areas as well, such as replacing doctors, lawyers, writers, painters, etc.
Responsibility- Who’s to blame when software or hardware malfunctions? Before AI, it was relatively easy to determine whether an incident was the result of the actions of a user, developer, or manufacturer. But in the era of AI-driven technologies, the lines are blurred. This can become an issue when AI algorithms start making critical decisions such as when a self-driving car has to choose between the life of a passenger and a pedestrian. Other conceivable scenarios where determining culpability and accountability will become difficult, such as when an AI-driven drug infusion system or robotic surgery machine harms a patient.
Technological Arms Race-Innovations in weaponized artificial intelligence have already taken many forms. The technology is used in the complex metrics that allow cruise missiles and drones to find targets hundreds of miles away, as well as the systems deployed to detect and counter them. Algorithms that are good at searching holiday photos can be repurposed to scour spy satellite imagery, for example, while the control software needed for an autonomous minivan is much like that required for a driverless tank.
According to experts, the technology could be used to better pinpoint bombing targets. This may lead to any autonomous weapons systems, the kind of robotic killing machines. To what extent can AI systems be designed and operated to reflect human values such as fairness, accountability, and transparency and avoid inequalities and biases? As AI-based systems are now involved in making decisions for instance, in the case of autonomous weapons. How much human control is necessary or required? Who bears responsibility for the AI-based outputs?
To ensure transparency, accountability, and explainability for the AI ecosystem, our governments, civil society, the private sector, and academia must be at the table to discuss governance mechanisms that minimize the risks and possible downsides of AI and autonomous systems while harnessing the full potential of this technology. The process of designing a governance ecosystem for AI, autonomous systems, and algorithms is certainly complex but not impossible.
Smarter Ai Through Quantum, Neuromorphic, And High
Achieving a smarter version of AI through Quantum computing, Neuromorphic computing, and High-performance computing.
The current AI and Deep Learning of the present era have a few shortcomings like training a deep net can be very time-consuming, cloud computing can be costly and unavailability of sufficient data can also be a problem. To be rid of these, the scientists are all set in their search for a smarter version of AI, and there seem to be three ways they can progress in the future.
High-Performance Computing (HPC)Within the process of improving AI, the most focus is on high-performance computing. It is based on the deep neural net but aims to make them faster and easier to access. It aims to provide better general-purpose environments like TensorFlow, and greater utilization of GPUs and FPGAs in larger and larger data centers, with the promise of even more specialized chips not too far away. The key drivers here address at least two of the three impediments to progress. These improvements will make it faster and easier to program for more reliably good results, and faster chips in particular should make the raw machine compute time shorter. The point of having a high-performance computer is so that the individual nodes can work together to solve a problem larger than any one computer can easily solve. And, just like people, the nodes need to be able to talk to one another in order to work meaningfully together. Of course, computers talk to each other over networks, and there is a variety of computer network (or interconnect) options available for the business clusters.
Neuromorphic ComputingNeuromorphic computing began as the pursuit of using analog circuits to mimic the synaptic structures found in brains. The brain excels at picking out patterns from noise and learning. A neuromorphic CPU excels at processing discrete, clear data. many believe neuromorphic computing can unlock applications and solve large-scale problems that have stymied conventional computing systems for decades. In 2008, the U.S. Defense Advanced Research Projects Agency (DARPA) launched a program called Systems of Neuromorphic Adaptive Plastic Scalable Electronics, or SyNAPSE, “to develop low-power electronic neuromorphic computers that scale to biological levels.” The project’s first phase was to develop nanometer-scale synapses that mimicked synapse activity in the brain but would function in a microcircuit-based architecture. Intel Labs set to work on its own lines of neuromorphic inquiry in 2011. While working through a series of acquisitions around AI processing, Intel made a critical talent hire in Narayan Srinivasa, who came aboard in early 2023 as Intel Labs’ chief scientist and senior principal engineer for neuromorphic computing.
Quantum ComputingThe current AI and Deep Learning of the present era have a few shortcomings like training a deep net can be very time-consuming, cloud computing can be costly and unavailability of sufficient data can also be a problem. To be rid of these, the scientists are all set in their search for a smarter version of AI, and there seem to be three ways they can progress in the future.Within the process of improving AI, the most focus is on high-performance computing. It is based on the deep neural net but aims to make them faster and easier to access. It aims to provide better general-purpose environments like TensorFlow, and greater utilization of GPUs and FPGAs in larger and larger data centers, with the promise of even more specialized chips not too far away. The key drivers here address at least two of the three impediments to progress. These improvements will make it faster and easier to program for more reliably good results, and faster chips in particular should make the raw machine compute time shorter. The point of having a high-performance computer is so that the individual nodes can work together to solve a problem larger than any one computer can easily solve. And, just like people, the nodes need to be able to talk to one another in order to work meaningfully together. Of course, computers talk to each other over networks, and there is a variety of computer network (or interconnect) options available for the business clusters.Neuromorphic computing began as the pursuit of using analog circuits to mimic the synaptic structures found in brains. The brain excels at picking out patterns from noise and learning. A neuromorphic CPU excels at processing discrete, clear data. many believe neuromorphic computing can unlock applications and solve large-scale problems that have stymied conventional computing systems for decades. In 2008, the U.S. Defense Advanced Research Projects Agency (DARPA) launched a program called Systems of Neuromorphic Adaptive Plastic Scalable Electronics, or SyNAPSE, “to develop low-power electronic neuromorphic computers that scale to biological levels.” The project’s first phase was to develop nanometer-scale synapses that mimicked synapse activity in the brain but would function in a microcircuit-based architecture. Intel Labs set to work on its own lines of neuromorphic inquiry in 2011. While working through a series of acquisitions around AI processing, Intel made a critical talent hire in Narayan Srinivasa, who came aboard in early 2023 as Intel Labs’ chief scientist and senior principal engineer for neuromorphic chúng tôi quantum computing, operations instead use the quantum state of an object to produce what’s known as a qubit. These states are the undefined properties of an object before they’ve been detected, such as the spin of an electron or the polarization of a photon. Rather than having a clear position, unmeasured quantum states occur in a mixed ‘superposition’, like a coin spinning through the air before landing. These superpositions can be entangled with those of other objects, meaning their final outcomes will be mathematically related even if they are unknown. Qubits can represent numerous possible combinations of 1 and 0 at the same time. This ability to simultaneously be in multiple states is called superposition. To put qubits into superposition, researchers manipulate them using precision lasers or microwave beams. With the help of this counterintuitive phenomenon, a quantum computer with several qubits in superposition can crunch through a vast number of potential outcomes simultaneously. The final result of a calculation emerges only once the qubits are measured, which immediately causes their quantum state to “collapse” to either 1 or 0.
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