AI for Improved Efficiency

One of the most significant benefits of AI is its ability to improve efficiency. By automating repetitive tasks, businesses can save time and money while also reducing errors. For example, AI programs perform various tasks, from scheduling appointments to analyzing data sets. The reliability and efficiency of these tools are why more and more business leaders are embracing AI to streamline their operations and stay competitive.

AI for Data Analysis

Data analysis is another area where AI is proving to be a game-changer. With the vast amounts of data, businesses generate daily, making sense of it can take time. AI algorithms can quickly analyze this data, identifying patterns and insights humans might miss. The ability of artificial intelligence to rapidly search, organize, and analyze extensive data is why more and more business leaders are turning to AI for data analysis.

AI for Cybersecurity

As businesses become more dependent on technology, the risk of cyber-attacks increases. AI tools can detect and prevent cyber-attacks by analyzing network traffic and identifying suspicious activity. Its level of proactive monitoring and quick analytics is why cybersecurity is one of the areas where AI is seeing the most significant growth. As a result, business leaders realize the importance of protecting their data and turning to AI to improve their cybersecurity measures.

AI for Enhanced Customer Experience

Another area where AI is making a significant impact is customer experience. Businesses can gain valuable insights into customer behavior and preferences by analyzing customer data, allowing them to tailor their products and services accordingly. Sasan Goodarzi understands the importance of customer experience and has been using AI to enhance the customer experience at Intuit. Intuit’s TurboTax, for example, now uses AI to provide personalized tax advice to customers, making it easier for them to file their taxes accurately.

The Role of Business Leaders in AI

Business leaders have a crucial role to play in the adoption of AI. It is up to them to ensure that their organizations are ready to embrace this technology and have the resources to make the most of it. Integrating AI requires a willingness to invest in AI technology and to prioritize it in their business strategy. One example of a business leader vocal about the importance of AI is Sundar Pichai, CEO of Google. Under his leadership, Google has made significant investments in AI research and development, and the company is now a leader in this field. With a willingness at the executive level to integrate AI for better business, companies can become competitive in the digital marketplace as consumers search for faster, more personalized experiences.

The Future of AI in Business

The potential of AI in the business world is enormous, and it is clear that we are only scratching the surface of what this technology can do. As AI continues to evolve, we can expect to see even more innovative applications emerge. From self-driving cars to virtual assistants, the possibilities are endless. Business leaders who are willing to embrace AI and invest in its development are likely to be the ones who thrive in the years to come.

AI is rapidly changing the business world, and business leaders who embrace this technology will likely reap significant rewards. The benefits of AI are evident, from improved efficiency to enhanced customer experience. However, business leaders must invest in AI and prioritize it in their business strategy to stay ahead of the competition. As AI continues to evolve, the opportunities for businesses will only grow, and those willing to embrace this technology will likely be the ones who succeed in the years to come.

Also Read: The Four Pillars Of Data Integrity

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Digital offers many opportunities for businesses in all sectors, but knowing where to start to maximize your business potential online can be challenging.

In this context, the democratization of artificial intelligence can play an essential role. This article explains how to improve your customer service with ChatGPT!

The Arrival Of ChatGPT

This is where ChatGPT comes in. ChatGPT is an artificial intelligence language model developed by OpenAI, which can be used for various purposes, including helping companies improve their business service through digital. Here is a non-exhaustive list of the benefits of using ChatGPT for your business:

• Compile searches
• Write SEO content
• Brainstorm
• Write computer code
• Automate parts of the sales process
• Provide after-sales service when customers purchase products
• Provide personalized instructions
• Optimize and improve processes through automation
• Translate text into another language
• Simplify the customer onboarding process
• Increase customer engagement
• And many other benefits and features!

Improve Communication With Your Customers.

Customer service is a crucial part of any business, and Chatgpt can help improve communication with your customers by providing quick and accurate answers to their questions. This allows your company to process many requests in less time than it should without necessarily needing additional staff.

Suppose you want to integrate ChatGPT directly into your site. In that case, you must use external tools such as Dialog Flow or IBM Watson or even plugins that allow you to create personalized ChatBot using ChatGPT language models. Once your ChatBot is set up, you must ensure it’s easy to use and answer customer questions accurately.

Personalize The User Experience.

Consumers expect a personalized experience when visiting a website, and ChatGPT can help you deliver that experience. By analyzing data from your website visitors, ChatGPT can offer personalized recommendations for products or services likely to interest your customers.

To personalize the user experience (UX), you must collect data about your website visitors. You can use data analysis tools like Google Analytics to track browsing behavior and user preferences.

If you want to know more about Google Analytics, we have written articles on this subject:

• Why use Google Analytics for your site?
• How do I upgrade to the new Google Analytics (GA4) version?

For maximum efficiency, you must ensure that the data collected is accurate and up-to-date.

Improve Sales Conversion

One of the significant benefits of using ChatGPT is improved sales conversion. By providing a personalized user experience, quickly responding to customer questions, providing personalized product recommendations, and processing online transactions, ChatGPT can help increase your online sales. Consumers are likely to buy from a company that provides responsive and personalized customer service.

To improve sales efficiency, you can integrate ChatGPT with your customer relationship management (CRM) system to track customer interactions, provide consistent responses, and follow the customer through the sales process.

Save Time And Efficiency, Reduce Operational Costs.

By using ChatGPT, your business can save time and be more efficient. ChatGPT can process large requests in real time, allowing you to focus on other aspects of your business. Moreover, artificial intelligence can help automate specific processes, improving your business’s efficiency.

Digital can help reduce operational costs by allowing companies to automate specific tasks.

To reduce operational costs, you can also use project management tools to optimize processes and maximize efficiency. You can also use digital marketing tools to target relevant audiences and reduce advertising costs.

We have seen how to improve your commercial service with ChatGPT and how artificial intelligence could be one of the keys to taking your business to the next level. As the tool is still recent, it may evolve and become even more efficient soon, or other AIs will emerge by then.

Also Read: 7 Risks Of Artificial Intelligence That We Must Face To Manage It Effectively

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A simple tip: one should begin by identifying the granular metrics used adaptively for a win-win bargain and optimize promotion as per demand. Let’s evaluate the key metrics for improving marketing strategies and measuring promotion effectiveness.

Important Metrics for Measuring Promotion Effectiveness

In order for your promotional campaigns to work as per your expectations, you need to measure the promotion effectiveness of past campaigns and resultant sales. Such insights are only available when you have certain metrics to measure; a few of which are elucidated below: –

Consider Empirical Data as Metrics for Measurement

Various factors impact the success of any promotional campaign; considering them for further evaluation can help businesses mint money with effective marketing strategies. Hence, careful granular data analysis can shed light on factors that boost or drop sales numbers. There are multiple sources from which such insights can be drawn effectively, such as: –

Tracking performance on a daily, weekly, and monthly basis to identify the change in trends early or account for ambiguous factors, like a change in weather, that can directly impact sales. Such data can help businesses take measures accordingly to ward off any hurdles during campaigns and ensure the effectiveness of promotional activities as per expectations.

Frequent visits from representatives for measuring promotional effectiveness can easily arrest out-of-stock situations at retailers’ end before they occur. Simply put, if your retailers are running out of stock during campaign weeks, your brand stands to lose potential buyers attracted by the campaigns. But reps maintaining healthy communication with the retailers are better positioned to identify such discrepancies before they occur, thereby saving your business from losing customers or brand reputation.

Measure shelf space to keep the flow of customers to your retail outlets steady. Believe it or not, extra shelf space and extra stocks are enough to draw your customers’ attention away from the competitors.

Track and Compare Competitor Market Share

A simple question – why do businesses spend dollars running promotional campaigns? 

Firstly, to spread knowledge about the availability of certain products in the market matching the ongoing demand.

Secondly, to attract potential and prospective buyers.

Thirdly, to expand the market for your products.

Unless owners have zero knowledge about the market share of their business and that of their competitors, creating effective campaigns will result in zero output; forget about measuring the promotion effectiveness. Hence, tracking market share and comparing it with that of the other players is to smoke out competition from the market.

Measure Category Growth

Category growth is as important as the company’s market share. A campaign also works by expanding the market of your products and bringing in a new category of buyers that was never your targeted demography. For instance, a brand selling skincare products does not necessarily target female buyers only. They introduce a new line of beauty products for men to draw them as prospective buyers. Category growth simply means creating demand in the market for new product lines.

Prioritize PoS Data

Businesses often focus on tracking shipment data. How many of your products are sitting in the storage or on the shelves of the retail outlets is not enough to measure promotion effectiveness. Rather, how many products are leaving the store in the hands of your buyers should be the focus area for analyzing campaign effectiveness.

Analyze Incremental Sales as Marketing ROI 

Tracking PoS data also helps understand the additional sales that occurred following promotion campaigns. Monitoring incremental sales against expected figures can help you understand how much extra business your company has made due to a promotion.

The metrics mentioned above are crucial to measuring trade promotion effectiveness only when such insights are available in real time and shared just when needed. Manual tracking and measuring of such metrics are time and labor-intensive. And, before the insights reach your hand, you have already lost a good amount of time and business to your competitors. 

Many businesses today resort to tech-based solutions to speed up the data extracting process and make strategies on real-time inputs to arrest the valuable time lost. 

Measuring Promotion Effectiveness with Machine Learning

If we consider consumer goods companies as an example, statistics suggest that over 500 billion dollars are invested by them in promotion campaigns annually. If the campaigns fail to generate the desired outcome, all the efforts, time, and labor are completely wasted. Hence, measuring trade promotion effectiveness is paramount – something we have already covered earlier on.

The main challenge arises when sufficient data to analyze the effectiveness is unavailable in real-time. This is where new-age technology solutions like AI and ML can make a striking difference.

Many software solutions are present in the market today to address the challenge mentioned above. These platforms leverage machine learning and artificial intelligence to help businesses understand, measure and enhance promotion effectiveness. These tech-based tools offer the following features: –

Data Management: Data is the foundation for making game-changing strategies. Timely availability of granular insights can make all the difference, even while planning promotion campaigns. A tech-based software solution powered by ML capabilities accommodates data acquisition, cleansing, enriching, and harmonizing data across the supply chain. Based on such inputs, campaigns can be planned, executed, and measured for effectiveness.

Casual Modeling: Machine learning-based casual modeling understands promotion performance and identifies empirical factors silently impacting the overall effectiveness of promotions.

Simulation and Prediction Modeling: Both of these consider various ‘what-if’ scenarios to adequately understand how campaigns will perform when given a specific scenario. Companies can effectively plan the most optimal promotion to run under user-defined constraints based on the input.

Benefits of Using ML Tools in Measuring Promotion Effectiveness

Using such advanced technology aims to gain optimum visibility into factors silently influencing campaign performances. Those insights are further utilized for a recommendation of optimized promotion parameters.

The following are a handful of benefits businesses can enjoy with these tools: –

• Gaining insights into promotion performance drivers
• Evaluating promotion effectiveness and ROI
• Optimizing campaigns based on real-time data

In a nutshell, promotion effectiveness helps enterprises measure, evaluate, and fully understand the effectiveness of trade promotions. But, in the absence of real-time insights, creating optimized promotions will remain a distant dream. Organizations will spend billions of dollars on campaigns that are incapable of generating enough business and adequate returns on investment. With the help of tech-based software solutions, the hindrance of data unavailability can be aptly addressed, accurate analysis across multiple metrics, like spend ratio, and volume uplift, amongst others, can be easily managed, and creating more optimized promotions will be possible.

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The data center campus in Tallion, the capital of Estonia, will have a set of modern technologies whose objective will be to optimize the facilities’ energy consumption as much as possible, helping to improve sustainability and reduce costs. These technologies are Building Management Software (BMS), EPMS (Energy and Power Management System) software, and White Space Cooling Optimization (WSCO) systems, all from the German firm Siemens.

These technologies are expanding in the industry with similar bets from large equipment providers for data centers, such as Schneider Electric. With this example, the technology company wants to demonstrate the potential of artificial intelligence to make data centers more sustainable. As explained in their announcement, these solutions can automatically adjust the cooling systems of the server rooms to take advantage of every last watt consumed, improving the adaptability of the systems to any change without generating excessive consumption.

These tools are managed through a dashboard that makes it easy to monitor and control power distribution systems and employs machine learning software to optimize cooling. The creators of these systems claim that their solution has allowed them to achieve a PUE of 1.2 in the data center, while the industry average is currently 1.6.

In his announcement, Kert Evert, director of development of Greenergy Data Centers, explains that “this new complex meets the highest international security standards and aims to operate with 25% more energy efficiency than the market average”. For his part, Dave Hopping, executive director of solutions and services at Siemens Smart Infrastructure, commented that “as the demand for data center services continues to increase globally, digital tools will play a key role in mitigating the environmental impact of data.

With the two future expansions planned by its creators, these facilities could become the most significant data center campus in the Baltic countries. It is expected that they consume energy from 100% renewable sources. Adding this to the strategy of using innovative technologies to optimize consumption, this project will be an example to follow in other geographies where the industry wants to continue expanding, following sustainable development objectives.

Also Read: 7 Risks Of Artificial Intelligence That We Must Face To Manage It Effectively

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Behavioral Impact

The first example we already see daily in many houses. Children treat virtual assistants rudely since Alexa or Siri obey without the need to ask them for things, please. The risk is that children can transfer these behaviors to their relationships with people, something that, as this Washington Post article warns, is already beginning to happen. More curious but less educated children.

Contrived Stupidity

It is getting harder and harder to fool the machines. But it is possible to do it. “We imagine that there is a small human brain inside the computer, but no, it’s just programming and mathematics,” explains Meredith Broussard, author of the book Artificial Stupidity. They are not infallible: there are makeup and costume techniques to fool facial recognition systems. As explained, work still needs to be done to achieve fully efficient Artificial Intelligence.

Bias And Lack Of Neutrality Of Machines

The artificial intelligence system used by judges in the US as an advisor has a bias and discourages black citizens from freedom more often than whites. The algorithm analyzes 173 variables —none of them is race— and gives a probability of recidivism from 0 to 10. The problem is not the machine but the risk that the judge delegates to it.

Artificial Intelligence Security

The video of Barack Obama insulting Donald Trump went viral a while ago in the United States. And yet it was false. Artificial Intelligence can become a great ally to manufacturing fake news if we do not use it with the appropriate ethics. In this specific case, the video is a creation of FakeApp, with the help of Adobe After Effects. This software uses machine learning to scan people’s faces in a video and impersonate them.

Unintended Consequences

In July 2017, all the alarms went off. Two Facebook chatbots had developed a language that their programmers did not understand. It was, however, a simple programming error. an expert in artificial intelligence at the CSIC, maintains that no machine has intentions, nor will they ever have them. “They can teach themselves to play Go and beat a champion, but they don’t know what they’re playing. If we put that same machine to distinguish photos of dogs and cats, it would forget everything else.

Robot Rights

Finally, the president of the Think Tank “We the Humans” asserted that robots should not have rights because they do not have responsibilities either. “Today a robot is not free, therefore it should not answer to anyone. It’s the people behind an intelligent system who will have to respond if the need arises,” he said.

Also Read: Tv With (AI)Artificial Intelligence, Discover The Best Models

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What Is A TV With Artificial Intelligence

Last year, the LG brand launched its first TVs with AI (Artificial Intelligence). This type of technology allows you to talk to the TV. Just by asking for any action by voice, LG televisions will be able to respond to you and carry it out. This LG ThinQ AI system allows for much easier communication. It is beneficial to ask about all kinds of doubts. Meanwhile, you can be watching a movie and find out who the director is and what happens in the film.

Likewise, you can ask him for all kinds of relevant information about your day to day life, such as what the weather is like or what time it is. It also allows complete control of the TV by voice, and it will enable you to increase or decrease the volume just by asking. So it makes the use of the television much easier. Even from any LG Smart TV with artificial intelligence, you can connect with the rest of your appliances. So you can also enjoy a fully connected home thanks to Home Connect technology.

Artificial Intelligence Televisions

We show you the televisions that can best adapt to your day to day life. The LG 49 UK 6200 PLA, 4K television, has all kinds of technology to view your content in high image and sound quality. This LG Ultra HD, 4K TV is the first TV with artificial intelligence. He answers you by voicing the answer when you ask him any questions. In addition, you can control the functions of the TV by voice.

The large screen of this 49-inch television has an Ultra HD 4K resolution with which great clarity and sharpness are achieved. Thanks to the Quad Core processor, image noise is reduced to show better quality. Likewise, it has an excellent acoustic level thanks to several virtual channels with an ultra-enveloping effect.

Suppose you are looking for a larger model with similar characteristics, the LG OLED55C8PLA TV. This model with artificial intelligence also allows you to communicate with it by voice as if you were talking to a natural person. In addition, it has a9 intelligent processor that provides detailed images with greater depth and brightness.

This LG Smart TV has a gallery mode with which images appear. In this way, the TV becomes an accurate picture. Thanks to the new Artificial Intelligence, enjoy all your multimedia content and communicate with your TV by voice.

Depending on the characteristics, the price of the TV with artificial intelligence may vary. Opting for a 49-inch model is not the same as a 65-inch one. Therefore, you can choose the television that best suits your needs and, above all, your budget.

Also Read: Are Smart TVs a Door To The Insecurity of Our Data?

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AI allows them to be detected predictively, that is before they occur. Thanks to this technology, companies can anticipate errors with a quality detection system that monitors the process in real-time with a high level of precision. This way, if the system identifies a faulty item, it can automatically remove it, before it affects later stages of the process. Thus, companies save costs and time.

AI And Quality Control In Companies

The quality and inspection phase is one of the most important in any manufacturing process since it is the one that guarantees that the level of the products is optimal. Artificial Intelligence can play a key role in this phase since, as already mentioned, it allows processes to be automated and failures to be predicted.

However, according to a study, many companies are not able to implement AI in their quality control processes. 43% of those surveyed maintain that it is due to a clear absence of an AI strategy and 42% to a lack of specific talent to implement it.

AI is not new, but not all companies get access to it, only 7% of companies use AI systems for quality control. Furthermore, those companies that do use AI focus on 4 use cases: Machine Learning, Big Data, Virtual Assistants, and Virtual Language Processing. However, its possibilities are enormous.

Helping Companies Optimize Their Quality Controls Through AI

Given the difficulties to start this technological transition, some companies seek to help their clients to achieve it successfully. Thanks to our experience, we help companies implement technological solutions to move towards digitization.

In addition to implementation help, we host webinars to share our knowledge and experience in working with customers. And, this is the case in the webinar How Applied AI can help you optimize your quality controls. In which our Applied Artificial Intelligence team explains how AI is incorporated into industrial processes, as well as the operation and the most relevant applications of this technology.

Within the different branches of AI, this webinar focuses on artificial vision and its application in production quality control. No matter what level of knowledge you have about this technology. Normally the audience we have either understands the concept, but has no experience in its implementation.

Artificial Vision In Quality Control

Artificial vision, applied to quality control, focuses on three aspects: inspection of parts, detection of defects, and process traceability. Until a few years ago, the companies that could apply artificial vision in their production were limited. However, with the growing monetization and democratization of technology and tools, more and more companies are committed to implementing it. 

Because right now? For three fundamental factors:

• Reducing hardware cost and installation complexity. Currently, almost all companies can access AI to apply it to their quality controls. Also SMEs.
• The rise of data. AI needs data to function. The more data you have about the business object, the more accurate your model will be.
• The increase in computational capacity and the creation of new models. In the last 10 years, the improvements in the algorithmic part have been remarkable. The cloud is evolving and, with it, its computing capacity. In addition, some pre-trained models allow you to achieve better results with a pay-per-use method.

AI Applied To Quality Control In The Textile Sector

Use cases in addition, concerning inference, they are dedicated to training operators and monitoring the model to ensure its correct operation. 

To implement the artificial vision in the specific case that José Alberto is talking about, our team had to meet several customer requirements. To begin with, in the first phase of data collection, the client did not have the internet. We had to get data in near real-time.

• To solve the lack of internet access, an external memory was used so that the operator could collect the visual inspection of the product, first in video, frames, and screenshots, and then with an endoscope camera. These tools made it possible to streamline the data collection process.
• Concerning inference, and to prevent the operator from wasting time in front of the camera, image processing times were cut. In addition, the same operator could see on the computer, in real-time, if the riveting was correct. At this point in the process, the internet was already available, so the images were uploaded to the cloud to verify that the process did not lose quality over time.

The Architecture Of Quality Control

Continuing with the same example to implement AI to help optimize your QA system, we work with an architecture based on Azure. These tools were Container Registry and IoT Edge Runtime, an application installed on each computer to be in permanent connection with the container. Connected to the IoT Edge through the cloud, we use the IoT Hub platform to send information and monitor the process.

AI In Quality Controls In Different Sectors

Beyond the textile sector, AI-optimized quality controls can be developed in other industries. Tires, high voltage towers, nuts. The applications are endless and the mission of this webinar is for them to leave with ideas for future applications. For example, in the case of tires, companies analyze them to see if they have defects. The analysis is manual, it is not carried out on 100% of the production, but a survey control is established.

Also Read: 5G Technology And Photovoltaic Roofs Proposals To Create The Road Of The Future

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In recent years, the market for collaborative robots, or cobots, has boomed in the industrial arena. These types of machines are designed to support human labor in industrial companies. That is to say, they do not imply the total automation of work but rather coexist with the workers to improve production methods.

Unlike traditional industrial robots, collaborative robotics is characterized by the ability to detect the presence of people and interact with them, too, among other things, to prevent accidents. These machines are more compact and lighter than conventional robots. They can be easily moved around the factory and are capable of performing different functions within the production chain itself.

With a lower cost, all these characteristics make collaborative robotics a more accessible option for SMEs than conventional robots. It will be possible to automate production in a more significant percentage of the industrial fabric thanks to it.

Collaborative Robotics And Smart Factories

The concept of industry 4.0 (or the fourth industrial revolution) would not be understood without the existence of Smart Factories. These intelligent factories are highly automated and digitized and use collaborative robotics to create a more efficient and fluid work environment.

Smart Factories put people at the center of their activity. Thanks to collaborative robotics, workers can focus on performing work that requires human capabilities. They are freed from routine, heavy or dangerous tasks, and optimized processes.

The most common collaborative robots are in charge of the “pick and place” function, that is, the task of picking up an object and placing it in another place. We can also find them in assembly lines or in the warehouses of intelligent factories, where they are capable of repeating the same movement with perfect precision.

Collaborative robotics reduces the health risks derived from tasks that require the use of force, forced postures, or the handling of dangerous substances. Furthermore, cobots are designed to ensure the safety of workers when interacting with them. Some of the most common uses of robots in factories are painting, welding or packaging, and handling pallets.

Also Read: Robotics – How Robots Will Help In Education

Safety Is A Priority For Collaborative Robots

Collaborative robots have several features that improve safety in Smart Factories. For example, they have low-inertia servo motors or elastic actuators, which offer a very reliable response and are made of light materials and rounded contours. The defining aspect of collaborative robotics is sensors that ensure safe interaction with people. According to the international standard ISO 10218, we find four types of robots according to their safety functions:

1. Safety Guarded Stop

These cobots typically work independently, performing a primary task, while the worker performs a secondary role. However, sometimes employees need to interact with the machine. When a human approaches a certain distance, this robot completely stops its activity. The safety monitored stop function is implemented in conventional robots utilizing special sensors.

2. Speed ​​And Separation Monitoring

In this case, the robot monitors its surroundings and can detect the proximity of workers. Unlike the first type, it progressively reduces its speed depending on the workers’ distance. If it gets too close, it can completely stop its activity. This feature of collaborative robotics is used in jobs that require the frequent presence of people.

3. Hand Guide

This type of robot can learn trajectories with the help of a person. This is a valuable function for quickly programming a new guideline, for example, for a pick and place machine. They are conventional robots equipped with a sensor like the two previous types.

4. Limitation Of Force And Energy

It is the most common and safest type of collaborative robot. At the same time, the former use standard industrial robots with additional sensors, these types of machines are specifically designed for interaction with people.

These cobots have all kinds of sensors that allow them to detect abnormal forces in their activity. This allows them to respond immediately when they contact a human. In this way, they can stop to avoid injury or dissipate some of the energy of the impact. In addition to being very light, these robots sometimes have an outer shell that absorbs shock.

Advantages Of Using Robots In Smart Factories

We have seen how safety is the main feature distinguishing collaborative robotics from traditional industrial robots, and its application can reduce and avoid risks and injuries characteristic of factories. In conclusion, collaborative robotics is a key tool for security understood as ” safety, “that is, the protection of employees.

The post Collaborative Robotics How Do Robots Help Humans In Industrial Work appeared first on Web Updates Daily.

But what can you contribute? Well, currently there are already platforms that apply the power of artificial intelligence to their products. Recently, events such as the Davis Cup have integrated functionalities of this type in the hands of the IBM company to carry out real-time analysis and offer the highlights of each game based on values ​​such as the reaction of the spectators, the players, or the intensity of each point. The results were truly spectacular.

In addition to this, other platforms such as Words with Friends, Overwatch, Scrabble Word Finder also use AI tools to offer real-time insights, statistics, and other very useful data for casters in the game. On the other hand, the power of streaming computing with platforms such as PsNow, Google Stadia, or Nvidia Geforce Now, are beginning to rely on AI to improve the user experience, analyzing game patterns, to scare, for example, bitrate or optimal data flow for each player at each moment, based for example on the activity and attention of the participant at each moment

Leveraging AI to improve eSports performance Esports has been setting the pace of gaming for a few years now, and providing useful tools to organize events, or provide real-time information for presenters and organizers, as well as for viewers, is a key point. AI can help for these purposes. Replay the most interesting part of the game at any given time, cut that clip by posting on Twitter. Offer the statistics of each player in real-time. Millions of possibilities at our fingertips

Cloud gaming, democratizing gaming And it is that Cloud Gaming platforms, or Video Game Streaming, are a reality and we cannot let them escape. Being able to play triple-A titles from any device, whatever it may be, anywhere in the world, without having to purchase expensive desktop computers with powerful graphics cards, is an element that is democratizing professional gaming, especially in those countries where their economy does not have access to these. The use and use of AI on these platforms can improve their quality and optimize their resources now that they are experiencing a huge boom.

Artificial Intelligence has proven its usefulness in almost any aspect of our daily lives, making many of our routine tasks faster, more efficient, and simpler. Gaming in the cloud could not be less, and it is beginning to obtain these advantages thanks to the massive use of AI in its different applications. AI is without a doubt in the presence of the cloud gaming world. And what do you think of the massive implementation of Artificial Intelligence in the world of Cloud gaming.

Also Read: Facebook AI [Artificial Intelligence] And Machine Learning Platform

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What Platforms Does Facebook Offer

Practically anything we need regarding Machine Learning we have at our fingertips included for free. Within their AI page, we can find various frameworks and tools. On the other hand, we can also find libraries and data sets, languages, discourses, and reasoning. Within each of these sections, there are various ML and AI platforms. Some of these platforms are even revolutionary in their sector, such as PyTorch, which is a Deep Learning framework which is a Python package designed to perform numerical calculations using tensor programming. It also allows its execution in GPU to speed up calculations.

The main advantage that PyTorch offers over other platforms is that it takes into account aspects of real life. To quickly understand this can be done with an example. If you throw two billiard balls, one against the other, the logical thing is that they collide. However, things like these are not contemplated on other platforms. They are in charge of controlling the movement of the ball, verifying its trajectory, and making sure that it remains a ball. However, they cannot take into account what would happen if it collided with something. In PyTorch, this can be done, since variables can be changed after a process has started. On the other platforms, you have to predefine all the possible variables at the beginning.

Another great platform is ONNX, which is responsible for representing Deep Learning models, which allows developers to move AI models between different platforms. ONNX supports many well-known frameworks such as PyTorch, Caffe2, Microsoft Cognitive Toolkit, and has added support for CoreML, TensorFlow, Qualcomm SNPE, Nvidia’s TensorRT, and Intel’s graph.

Now that we know different frameworks, let’s get to know other sections that are also very interesting. One of the available platforms is the translator. Every time we realize that there are more platforms of translators available. However, each one has sections that others do not have and it is always good to be able to compare different platforms and choose the one that seems best in practice. In addition to this, Facebook has a platform called ParlAI, which is compatible with PyTorch and TensorFlow. This is responsible for collecting voice-to-text data and understanding it, even being able to answer questions as chatbots currently do.

To finish, it is necessary to name a tool that will be increasingly necessary for the future. We are talking about the tool dubbed Hateful Memes. This is in charge of addressing a great challenge today, such as classifying memes according to whether they really try to cause laughter, or on the other hand, simply try to be hateful to some group of people or even directed towards a single person. The big problem with this is that memes are usually a photograph accompanied by text. It is easy for us to understand that photography and text go together to give it meaning. However, a machine would interpret them separately. That is why Hateful Memes has developed a way to classify memes into 3 different types:

1- Predominantly text

2- Predominant image and text

3- Predominant image

By cataloging them in these 3 groups it is easier to understand what the meaning of each meme is. Once the machine has been able to understand the meaning that is given to that meme, it is time to classify whether it is hateful or not, thus managing to eliminate posts that are harmful to various groups of people. To conclude, we must add that Facebook is doing a great job in developing its AI and highlighting how important it is to allow other people to try its AI for free and understand how it works. In this way more people will join the development of ML and who knows, maybe a decade from now we will have ML tools capable of performing tasks exactly as a person would.

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