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Why Artificial Intelligence is a Game-Changer in Stadium Security

Stadiums must always stay up to date with the latest security technologies to ensure they offer their customers and staff the maximum level of safety. This is especially true for large venues that host tens of thousands of people.

Among the various risks spectators and stadium workers face, we find violent hooliganism, theft, vandalism, drug dealing, and even terrorism. Traditional stadium security measures to counter these threats include metal detectors, security cameras, and bag inspections. However, by themselves, they can’t always provide the desired level of security.

Fortunately, the introduction of artificial intelligence (AI) provides stadiums with new ways to minimize or entirely neutralize the risks that exist. Let’s focus on how AI-powered security works and how stadiums can benefit from it.

Analyzing Large Quantities of Data From Multiple Sources

What previously would have taken days or weeks can now be done in seconds or minutes, thus drastically shortening the response time.

For instance, by analyzing a network of security cameras, an AI system can detect unusual patterns, such as large groups gathering in a certain area or people loitering for extended periods of time. The system can also monitor existing lines to detect long wait times and can be used to respond rapidly to problems before they escalate into something more serious.

Detecting Threats Faster and More Effectively than Traditional Metal Detectors

People trying to access stadiums while carrying weapons have always been a major security concern for stadium managers.

Traditional metal detectors are still reliable tools to detect concealed weapons. However, they are often slow and inaccurate, as they need human operators to assess the scans.

An AI-powered system can make searches quicker and more efficient by automatically scanning people as they walk in. Spectators can enter the stadium at a regular walking pace, and AI will alert security issues only when an issue is detected. AI weapons detectors tend also to be more accurate than traditional metal detectors, which often detect metal objects that are not a weapon and may fail to detect weapons that are not made of metal.

Monitoring Spectators’ Behavior and Detecting Threats

AI can also be used to detect suspicious behavior among spectators. An AI system can analyze a camera feed in real-time and look for patterns indicating aggressive or dangerous behavior. For example, an AI system may recognize when people start shouting or running, which could indicate a fight or other incidents.

In this way, security personnel can take action before it is too late and stop potential threats before they escalate further.

Recognizing Security Shortcomings and Suggesting Improvements

In addition to detecting and responding to security threats, AI can also help stadiums improve their current security measures. These systems can detect weak areas in the security infrastructure and inform managers of potential vulnerabilities.

For example, an AI system may recognize that certain areas are not monitored by CCTV cameras and suggest improvements such as installing additional cameras. The AI system can also recommend how to strengthen access control procedures or even offer tips on where staff members should be stationed for maximum effectiveness.

Freeing Security Staff for Other Activities

Finally, AI can also be used to free up security personnel from tedious tasks and allow them to focus on more important activities. For instance, by automating the process of searching through video security footage for suspicious behavior, AI systems can greatly reduce the amount of time security personnel need to spend examining said footage. This allows them to dedicate their time and energy to other activities, such as patrolling or responding to emergencies.

Enhancing Cybersecurity

In addition to physical threats, stadiums are also exposed to the risk of cyberattacks, which may result in personal data theft and the disruption of operations. In turn, this may lead to significant financial loss and reputational damages.

By leveraging AI, Stadiums can respond to cyber threats quickly and effectively. For example, AI systems can also be used to monitor access logs and detect abnormal behavior that may indicate a cyberattack. AI-powered solutions can also be used to detect malicious software and prevent it from entering the stadium’s networks.

Cutting Security Costs

The features we described in this article can also help stadiums achieve significant savings in terms of security costs. AI-powered systems can reduce the need for a large physical security staff, which in turn reduces payroll costs. Moreover, AI systems can save time, which in turn translates into cost savings.

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How Artificial Intelligence Is Influencing Our Daily Lives

The word artificial intelligence (AI) brings the pictures of robots and machines in our mind. Surprisingly AI has already melded as a part and parcel of our daily lives. “Some people call this artificial intelligence, but the reality is this technology will enhance us. So instead of artificial intelligence, I think we will augment our intelligence”- Ginni Rometty. Chairman, President & CEO of IBM. AI has a great influence on human lives and it will dramatically grow in the years to come. The following are the ways in which AI has delved into human lives:

Virtual Personal Assistant

It all starts by saying “Ok Google”. All intelligent personal assistants like Siri, Google Now and Cortana are available on various platforms which help to find useful information when we ask with our own voice. Say for example “Remind me to book tickets today” and the assistant will give a quick response by digging more information, relying on existing data on your phone and sending requests to other applications. Artificial intelligence plays a crucial role in retrieving the data and further using the information to deliver best results. For example, Microsoft’s Cortana meticulously learns about its users and is able to anticipate their actions. Virtual intelligent assistants can process a huge amount of data from various sources to learn and understand more about the users.

Prediction on Purchase

The e-commerce industry is highly dependent on predicting the purchases by the consumers. Amazon is developing an anticipatory shipping project which forecasts to send the items to the customers. This will completely eliminate the need for a last-minute visit to online stores. On the other hand, offline stores also work in a similar way. When customers visit them, sometimes they are awarded coupons which have been selected in accord with predictive analytics algorithms.

Fraud Detection

We all receive emails or statements regarding the use of our debit/credit card at a specific point of sale. Most of the banks send these messages to reduce the chances of fraudulence in the account. AI is the technology deployed to monitor fraudulency. In most of the cases, algorithms are given a large sample of fraudulent and non-fraudulent purchases and asked to look for signs that a transaction falls into one category or another. After repetitive processes, systems will be able to spot a fraudulent transaction based on the indicators it had learned through a previous training exercise.

Music and Movie Recommendation

Must have apps like Spotify, Pandora and Netflix push out recommendations to us based on the interest and experiences made in the past. This is made by closely monitoring the choices customers make and then inserting an algorithm helps the apps to provide recommendations. For instance, a song might have “guitar riffs” listed as characteristics; if someone likes that song there is a probability that the same person will like other songs with same characteristics. This is the foundation of recommendation services which helps customers to explore more.

News Generation

AI programs can write about flabbergasting news all around the globe. For example, Yahoo uses AI to write simple financial summaries, sports recap and fantasy sports reports.

Online Customer Support

Our smartphone, car and bank all use artificial intelligence on a daily basis. Sometimes it’s obvious by playing around with google assistant saying “Ok Google”. Sometimes it’s less obvious, like when you make an abnormal purchase on your credit card and don’t get a fraud alert from your bank. AI is everywhere, and it’s making a huge difference in our lives every day.

What Is Artificial Intelligence (Ai) And How Does It Improve Sales?

AI is a subfield of computer science that enables machines to imitate human thought, decision-making, and behavior. According to massive data sets, these technologies learn and adapt, allowing them to make insights, forecasts, and recommendations. This technology has been gradually introduced into several aspects of our everyday life, including internet chatbots, facial recognition software, and self-driving cars.

The potential for AI in sales is huge, but yet to be fully unlocked. It can be used by businesses to improve lead volume, closing rate, and revenue performance by helping automate much of the process. Salesforce also found that high-performing teams are 4.9X more likely to be using AI than underperforming ones.

How is AI Used in Sales?

Sales intelligence tools are the best solutions available for those who are looking to achieve this goal of finding and qualifying prospects quickly. Tools such as Triggr will help B2B sales & marketing professionals generate highly targeted lead lists at scale, complete with contact details of decision-makers who matter.

Artificial Intelligence Is The Pharmaceutical Expert In Drug Development

Artificial intelligence has the potential to identify the right pharmaceutical component in drug development

The

AI platform is used for drug development

Drug development is a long process if conducted manually. Initially, researchers have to identify the target protein that is causing the disease and study it for a long time. Next, they try to find which component or a molecule would influence the protein. During this process, researchers make sure that inefficient components are kept aside and only safe, efficient components are taken further. The role of AI in drug discovery starts with finding the molecule that better address the protein. Researchers can’t test the hundreds and thousands of molecules in market. It is both lengthy and expensive. Fortunately, AI platforms replace the long testing process with a simple analysis. Researchers feed in parameters into the AI platforms and make them run an analysis on the molecules. AI platform identifies the right component that can be used for drug development.

Applying neural network in drug discovery

Even though deep neural network has been around the tech radar for decades, it got a wide range of attention only in 2012. Researchers from the University of Toronto won the ImageNet Large Scale Visual Recognition Challenge (ILSVR) are using deep neural network. Currently, pharmaceutical companies are using various types of deep neural networks to explore classical statistical techniques. The technology helps in finding the right molecule that is responsible for certain activities. Deep neural network gives an immediate indication to chemists of what to do in order to remove certain unwanted activities. This deep neural network model is also used by chemists to judge their compound ideas before deciding on whether to synthesize them or not

Big data fed into AI helps drug development

Healthcare data is huge and critical. Today, millions of research, feedback, reports, patient records and a whole lot of other things related to the healthcare industry are fed into AI in form of big data. Even though healthcare sector is pretty slow in availing solutions from them, medical institutions are trying their best to stay ahead in the race. Artificial intelligence systems are featured with an apt mechanism to go through data and make meaningful interpretations out of that. Deep learning programs run on the data and learn more about the proteins whose presence makes a difference between healthy patients and an ill one. Meanwhile, machine learning abilities strive to find and establish connections between proteins and diseases.

AI in Phase wise drug discovery

Before the AI in drug discovery (Phase 1): Discovering the right drug involves reading and analysing already existing literature and testing the ways potential drugs interact with targets. AI performs the tasks faster than humans and provides rapid results. AI in preclinical development (Phase 2): During the preclinical development phase, the drug is tested on animals to see how they perform. Unveiling AI in this phase will help trials run smoothly and enable researchers to more quickly and successfully predict how a drug might interact with the animal model. AI in clinical trials (Phase 3): Researchers begin testing the drug on human bodies during the clinical trial. AI can facilitate participant monitoring during clinical trials, generating a larger set of data more quickly and aid in participant retention by personalizing the trial experience.

The ethical drawback

The pharmaceutical industry is a slow learner when it comes to implying digital health technology. Pharma companies have so far delayed the idea of using artificial intelligence and machine learning strategies to develop drugs. Artificial intelligence has the potential to make extraordinary innovation wave in drug discovery. However, the pharmaceutical sector should work on filling the gap between understanding these possibilities and applying them to the drug discovery and development process. Healthcare industry has rapidly embraced artificial intelligence into the working system. AI and its sub-technologies are helping the medical industry on a large scale. However, the pharmaceutical industry is still on the initial stage of leveraging digital technologies to accelerate the drug development process. The main goal of drug discovery is to identify the medicine that acts beneficially on the body. Finding the right drug involves a lengthy process of carrying out large screen libraries of molecules that can specifically bind to a target molecule involved in a disease. The mission to find the right drug goes through numerous rounds of tests to develop it into a promising compound. According to Taconic Biosciences’s tally, an incredible amount of time and money goes into drug development and bringing a drug to market costs about US$2.8 billion over 12+ years. Fortunately, artificial intelligence can help pharmaceutical industry to find the right drug and develop it. Artificial intelligence uses personified knowledge and learns from solutions it produces to address not only specific but also complex problems in chúng tôi development is a long process if conducted manually. Initially, researchers have to identify the target protein that is causing the disease and study it for a long time. Next, they try to find which component or a molecule would influence the protein. During this process, researchers make sure that inefficient components are kept aside and only safe, efficient components are taken further. The role of AI in drug discovery starts with finding the molecule that better address the protein. Researchers can’t test the hundreds and thousands of molecules in market. It is both lengthy and expensive. Fortunately, AI platforms replace the long testing process with a simple analysis. Researchers feed in parameters into the AI platforms and make them run an analysis on the molecules. AI platform identifies the right component that can be used for drug chúng tôi though deep neural network has been around the tech radar for decades, it got a wide range of attention only in 2012. Researchers from the University of Toronto won the ImageNet Large Scale Visual Recognition Challenge (ILSVR) are using deep neural network. Currently, pharmaceutical companies are using various types of deep neural networks to explore classical statistical techniques. The technology helps in finding the right molecule that is responsible for certain activities. Deep neural network gives an immediate indication to chemists of what to do in order to remove certain unwanted activities. This deep neural network model is also used by chemists to judge their compound ideas before deciding on whether to synthesize them or notHealthcare data is huge and critical. Today, millions of research, feedback, reports, patient records and a whole lot of other things related to the healthcare industry are fed into AI in form of big data. Even though healthcare sector is pretty slow in availing solutions from them, medical institutions are trying their best to stay ahead in the race. Artificial intelligence systems are featured with an apt mechanism to go through data and make meaningful interpretations out of that. Deep learning programs run on the data and learn more about the proteins whose presence makes a difference between healthy patients and an ill one. Meanwhile, machine learning abilities strive to find and establish connections between proteins and diseases.Before the Covid-19 pandemic outbreak , no one thought that a vaccine process could be fast-tracked so much. Generally, making a vaccine and testing it on a trial basis involves years of research and observation. However, the pandemic has broken the routine. Governments across the globe were running a race to come up with an effective vaccine as soon as possible. The funding into pharmaceutical industry also skyrocketed during the period. With accelerating the trials and emergency approvals on the bag, pharmaceutical companies leveraged AI to complement the vaccine making process.Discovering the right drug involves reading and analysing already existing literature and testing the ways potential drugs interact with targets. AI performs the tasks faster than humans and provides rapid results.During the preclinical development phase, the drug is tested on animals to see how they perform. Unveiling AI in this phase will help trials run smoothly and enable researchers to more quickly and successfully predict how a drug might interact with the animal model.Researchers begin testing the drug on human bodies during the clinical trial. AI can facilitate participant monitoring during clinical trials, generating a larger set of data more quickly and aid in participant retention by personalizing the trial chúng tôi though AI is helping drug discovery to a large range, it also raises some remarkable ethical questions. Patient data are hectic in healthcare industry. If these critical data gets to the hands of hackers, there are chances that it’ll be used for evil purposes. Henceforth, patient privacy needs to be maintained. Unlike many other sectors, there are no regulations or policies that direct drug makers to go on a drawn line. It is up to the pharmaceutical companies to secure patient data and use it in the right way.

Artificial Intelligence (Ai) In Supply Chains

Applying artificial intelligence (AI) is one way supply chain professionals are solving key issues and improving global operations.

AI-enhanced tools are being used throughout supply chains to increase efficiency, reduce the impact of a worldwide worker shortage, and discover better, safer ways to move goods from one point to another.

AI applications can be found throughout supply chains, from the manufacturing floor to front-door delivery. Shipping companies are using Internet of Things (IoT) devices to gather and analyze data about goods in shipment and track the mechanical health and constant location of expensive vehicles and related transportation tools.

Customer-facing retailers are using AI to gain a better understanding of their key demographics to make better predictions about future behavior. The list goes on — anywhere some goods need to make it from point A to point B, there’s a good chance AI is being used to enhance, refine, and analyze supply chain operations.

Some of the benefits derived from AI in supply chains are less tangible than others. For example, determining the impact of predictive analytics based on supply chain data can eventually yield benefits, but some companies are reporting a direct link between revenue shifts and the addition of AI in supply chains.

For more: Top Performing Artificial Intelligence Companies

Automation with AI for supply chain tasks can reduce time and money spent on traditionally manual tasks. Supply chain tasks that can be automated for businesses include:

Warehouse robotics:

A company can use automated systems and specialized software to move materials and perform other tasks.

IoT:

Automation can also offer IoT which are physical tools with sensors, processing ability, and software that connects and sends or receives data with other devices or other communications networks.

AI/ML:

Artificial intelligence (AI) and machine learning (ML) can help automated supply chains to learn and expect user activity.

Predictive analytics:

Predictive analytics helps automate supply chains using data mining, predictive modeling, and machine learning to analyze past and current facts to make predictions about what may happen in the future.

Digital process automation (DPA):

DPA automates multiple tasks for the supply chain across applications.

Optical Character Recognition (OCR):

OCR is a form of text recognition that helps supply chains.

AI automation is a game-changer and a necessity for any supply chain to keep up with the fast-moving industries.

For more tools for supply chains: 15 Best Data Warehouse Software & Tools

Artificial intelligence developments are increasing among businesses, assisting with a company’s development and planning. AI is used to find and identify risks in a company’s infrastructure.

Listed are more benefits of using AI in supply chains:

Increases productivity:

AI techniques, such as automation, saves a company time so their employees can focus on higher-level tasks instead of tasks that can be done through automation.

Constant visibility:

If a company needs it, the AI tools can operate without any breaks or downtime.

Used by experts and beginners:

AI increases the capabilities of employees who are not experts in their business’s technology tools.

Decision-making easier:

AI makes the decision-making process easier, increasing decision speeds and making smarter decisions.

While artificial intelligence has an abundance of benefits, no technology is perfect. AI is growing and changing every day meaning the technology will become outdated or not meet a company’s needs.

Listed are the challenges supply chains may face with AI:

Difficult Scalability:

AI requires a large amount of data to work effectively, so AI/ML can create algorithms, prediction models, and analysis of insights.

Lack of trust in AI:

With recent developments in AI, companies can be hesitant to consider them for their supply chains. Computers also do not have the same capabilities as a human would, making it difficult to make the switch.

AI technology constraints:

While AI is a positive tool, it is a new tool and not fully developed. There may be tasks a company wants to automate that cannot be or will take more of the company’s time rather than the deducting time.

High costs:

While AI technology can save time and money, the initial cost can be expensive for many supply chains. Integration and operating processes can also cost more than a company wants to spend.

AI machines can be complicated especially if they need replacement or updates. However, with the correct AI solution, supply chains can benefit from AI tools.

Machine learning is being used to identify patterns and influential factors in supply chain data with algorithms and constraint-based modeling, a mathematical approach where the outcome of each decision is constrained by a minimum and maximum range of limits. This data-rich modeling empowers warehouse managers to make much more educated decisions about inventory stocking.

This type of big data predictive analysis is transforming the way warehouse managers handle inventory by providing deep levels of insight, which would be impossible to unravel with manual, human-driven processes and endless, self-improving forecasting loops.

C3 AI uses AI to power its Inventory Optimization platform, which gives warehouse managers data on inventory levels in real-time, including information about parts, components, and finished goods. As the machine learning ages, the platform produces stocking recommendations based on data from production orders, purchase orders, and supplier deliveries.

In a world where just about anything can be ordered online and delivered within data, companies that don’t have a firm handle on delivery logistics are at risk of falling behind. Customers today expect quick, accurate shipping, and they’re all too happy to turn somewhere else when a company is unable to deliver on that expectation.

McKinsey & Company reports that around 40% of customers who tried grocery delivery for the first time intend to keep using these services indefinitely. Customers in major markets like New York and Chicago have dozens of choices.

AI-driven route optimization platforms and GPS tools powered by AI like ORION, a company used by logistics leader UPS, create the most efficient routes from all the possibilities, a task untenable with conventional approaches, which have been inadequate for fully analyzing the myriad route possibilities.

IoT device data and other information taken from in-transit supply chain vehicles can provide invaluable insights into the health and longevity of the expensive equipment required to keep goods moving through supply chains. Machine learning makes maintenance recommendations and failure predictions based on past and real-time data. This allows companies to take vehicles out of the chain before performance issues create a cascading backlog of delays.

Chicago-based Uptake uses AI and machine learning to analyze data to predict mechanical failures for a wide range of vehicles and cargo containers, including trucks, cars, railcars, combines, and planes. The company uses data from IoT devices, GPS information, and data pulled directly from vehicle performance records to arrive at its predictions, which can greatly reduce downtime.

Supply chain management includes a great deal of detail-oriented analysis, including how goods are loaded and unloaded from shipping containers. Both art and science are needed to determine the fastest, most efficient ways to get goods on and off trucks, ships, and planes.

Companies like Zebra Technologies use a combination of hardware, software, and data analytics to deliver real-time visibility into loading processes. These insights can be used to optimize space inside trailers, reducing the amount of “air” being shipped. Zebra can also help companies design quicker, less risky, and more efficient processing protocols to manage parcels.

Moving goods around the world are expensive, and only becoming more expensive. Bloomberg reports that the cost of moving goods by ship, for example, increased by 12% in 2023, the highest level in the five years before.

AI in supply chain innovations are paving the way for a future where we can eventually expect to see AI-powered, autonomous vehicles used throughout supply chains. The data these platforms are mining and analyzing today will continue improving the cost and efficiency of an increasingly complicated global supply chain.

For more information of AI task management: Anticipating the Birth of AI Employee Clones

AI in supply chains creates stronger efficiency, visibility, and optimization. Implementing AI can benefit and help their business practices. AI can be a large part of evolving a supply chain company and help with adapting to supply chain problems.

One of the benefits of AI is its ability to predict action outcomes. Supply chains can try this capability to make their operation more efficient with AI simulations.

Using a simulation, supply chain businesses have more flexibility to optimize operations using real-world scenarios in the process. AI simulation tools can be effective for many parts of the supply chain.

Through AI simulation, supply chain managers can make an exact digital copy of the entire warehouse they work in. Then the AI logistics can use a simulation on the digital copy to try different optimization strategies.

If a supply chain is running inefficiently, it could cause serious problems throughout the supply chain. AI can help automate different parts of their warehouses through inventory management, which can save both time and money if used correctly.

IoT tags are also a tool that can help keep track of the status of different items. The IoT tags communicate to an AI hub that manages all of this inventory data updates on data changes. The AI can then alert the supply chain company with any problems.

Cybersecurity is a necessary part of handling data and is now vital for any supply chain company. Cyber attacks are common, with cybercriminals using different tactics to steal data and sensitive information. Using AI can help protect a supply chain company’s infrastructure.

AI is a highly effective tool to help stay ahead of changes or risks as AI on supply chains can recognize what patterns are most common and when they may change.

A supply chain company can use AI to monitor login activity, traffic, and any irregular processes on its servers. AI can alert the company about the change.

Supply chains can use AI data analysis to see what supply and demand might look like in upcoming quarters. AI algorithms can analyze data to predict how much and what product will be in demand.

Demand forecasting can allow different links in the supply chain to reduce supply strain. If the supply chain business knows how much of a product they will need, they can use it as a better way to decide on the amounts they need.

Due to the capabilities of ML, systems can learn to allow different processes such as infrastructure vision to learn how to automate with the supply chain company’s needs.

Along with ML and AI, IoT devices can collect data on how many materials are being used. AI data analysis algorithms can identify where the materials are being used and what materials are being wasted.

AI in supply chains will be a part of innovating a better supply chain process to create more efficient supply chains in the future. Every part of the supply chain can implement AI to automate tasks, improve operations, and strengthen cybersecurity practices.

With AI tools, supply chain businesses can evolve and grow to create a positive change in their business and meet new supply chain challenges.

For more information, also see: AI and Deep Learning

Understanding The Philosophy Of Artificial Intelligence

The blind eye towards the philosophy of artificial intelligence

Giant companies dealing with artificial intelligence are setting benchmarks and crossing milestones today. philosophy of artificial intelligence is the most underrated.

A fun game for philosophers: AI and Philosophy

Given the fact that AI is a discipline, it happens to be of considerable interest for philosophers. These philosophers strive to read between the lines of AI and their developments. They try to perceive every nook and cranny with logic which also involves deconstructionism, deontologic attempts that adhere to the doxastic attitudes. The strong and the weak AI In simple terms,

How does philosophy affect artificial intelligence?

John McCarthy argues that the philosophy of artificial intelligence is important to consider as it impacts the practice of artificial intelligence. It helps the practitioners to harp upon the most challenging questions, the ones which seem unanswerable.

Do we need mind to be intelligent?

This is that one question has pains the minds of not only philosophers but AI maestros as well. There are conflicting opinions and views about it. Alan Turing, in his days, had opined that any machine that can imitate a human is intelligent and formulated a test known as The Alan Turing test. In his view, a machine that is able to behave like humans can pass the test. Intelligence is not required. But this view continues to meet with criticisms as philosophers who are AI-fearing people in general.

Consciousness vs Intelligence

There is always a tug of war between consciousness and intelligence and pretty much the war is between philosophers and scientists. While philosophers mostly stress on the presence of a mind to be intelligent, scientists defy the argument by prioritising consciousness. In case of machines, as accorded by scientists of all ages, consciousness is the most important power to possess. This can be better understood with the Chinese room experiment.

The Chinese room experiment

Searle who had devised this experiment and chose himself for the sample, locked himself up in a room. There were Chinese people standing outside the room. These people were supposed to send him cards written something on them in Chinese. Searle on the other hand, had no knowledge of the language Chinese. When they slipped their cards into the room, Searle used a translator to answer them back. Fun part was that the Chinese did not suspect that the person inside lacked knowledge of the language Chinese and were convinced by the answers that were reverted to them in Chinese . This experiment is a great explainer of how consciousness works than intelligence. Being conscious of the gadgets available and in situations that can be used is something that is executed using consciousness. Intelligence in this would have been to decode the Chinese language based on certain symbols because that is what the language consist of- a set of symbols. Incessant debates The debates on the philosophy and artificial intelligence cannot find any respite because one cannot talk in absolutes in matters like these.

Giant companies dealing with artificial intelligence are setting benchmarks and crossing milestones today. Artificial intelligence is now a universe in itself, a commerce that tops every other forms of business, an entity that every business uses. However, it is important to know and remember that everything that is good is driven by some philosophy. Artificial intelligence too is enshrouded in philosophy which the majority is not aware of. Hence theis the most underrated.Given the fact that AI is a discipline, it happens to be of considerable interest for philosophers. These philosophers strive to read between the lines of AI and their developments. They try to perceive every nook and cranny with logic which also involves deconstructionism, deontologic attempts that adhere to the doxastic chúng tôi simple terms, strong AI aims to create humanoids or human like robots that are capable surpassing human intelligence. Weak AI , on the contrary, is what adhered to by most companies. The machines that are designed to perform human tasks and emulate human skills. The focus area of philosophers is judge the potentials of the two. Although, most philosophers hold a grudge against the strong AI and suggest to overthrow it. They accord that strong AI is likely to cause damage unlike weak AI that contributes to the progress of a chúng tôi McCarthy argues that the philosophy of artificial intelligence is important to consider as it impacts the practice of artificial intelligence. It helps the practitioners to harp upon the most challenging questions, the ones which seem chúng tôi is that one question has pains the minds of not only philosophers but AI maestros as well. There are conflicting opinions and views about it. Alan Turing, in his days, had opined that any machine that can imitate a human is intelligent and formulated a test known as The Alan Turing test. In his view, a machine that is able to behave like humans can pass the test. Intelligence is not required. But this view continues to meet with criticisms as philosophers who are AI-fearing people in general.There is always a tug of war between consciousness and intelligence and pretty much the war is between philosophers and scientists. While philosophers mostly stress on the presence of a mind to be intelligent, scientists defy the argument by prioritising consciousness. In case of machines, as accorded by scientists of all ages, consciousness is the most important power to possess. This can be better understood with the Chinese room experiment.Searle who had devised this experiment and chose himself for the sample, locked himself up in a room. There were Chinese people standing outside the room. These people were supposed to send him cards written something on them in Chinese. Searle on the other hand, had no knowledge of the language Chinese. When they slipped their cards into the room, Searle used a translator to answer them back. Fun part was that the Chinese did not suspect that the person inside lacked knowledge of the language Chinese and were convinced by the answers that were reverted to them in Chinese . This experiment is a great explainer of how consciousness works than intelligence. Being conscious of the gadgets available and in situations that can be used is something that is executed using consciousness. Intelligence in this would have been to decode the Chinese language based on certain symbols because that is what the language consist of- a set of chúng tôi debates on thecannot find any respite because one cannot talk in absolutes in matters like these. However, the philosophy of AI provides scientists and practitioners to strive to resolve the grey areas. And the constant challenges posed by the philosophers of AI opens rooms for channelising the improvements for artificial intelligence.

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