Human-machine collaboration is transforming the way we think about jobs, tasks, and roles as machines take on many of the mundane tasks that used to be only done by people. From AI-powered automation to data analysis, human-machine collaboration is increasingly becoming integrated into almost every industry. In this article, we will explore how human-machine collaboration is revolutionizing the future of work and the challenges and opportunities it offers.
At its core, human-machine collaboration is about leveraging technology to enable humans to do what they do best – think creatively and solve problems – while machines take care of mundane tasks such as data entry or analysis.
Human-machine collaboration involves combining human creativity and Artificial Intelligence (AI) to create something new, which could be anything from writing a story, designing a product, or even solving complex problems. Hence, combining the unique abilities of humans and machines allows us to do what would take much longer to complete on our own.
According to a survey, 87% of companies worldwide believe AI technologies can give them an edge over their competition. With this remarkable figure in mind, there is no doubt that implementing human-machine collaboration can offer several benefits and open up new possibilities for businesses to improve their performance and create a more efficient workplace.
From a business perspective, human-machine collaboration helps reduce errors while improving efficiency. Machines can do the work much faster than humans, allowing more work to be done in less time.
Hence, using AI, businesses can automate repetitive tasks, allowing employees to focus on higher-value activities. According to a survey, AI can potentially increase worker productivity by an estimated 40% by 2035, improving efficiency and faster task completion.
Combining human insights with machine intelligence can provide unprecedented data accuracy, analysis, and insights that could not have been achieved using only one or the other. By leveraging AI-driven insights to complement human judgment, companies can make more informed decisions about their operations, products, and services, which is key to unlocking the full potential of any organization’s data and analytics capabilities.
By 2025, it is estimated that 95% of all customer interactions will be handled by AI-enabled solutions, indicating a huge shift in how businesses and customers interact. Indeed, leveraging AI algorithms enables companies to provide faster and more accurate customer service, increasing customer satisfaction. This, in turn, leads to higher conversion rates and increased revenue for the business.
AI can also help businesses automate mundane tasks such as responding to customer inquiries or requests, which frees up employees’ time to focus on more complex tasks such as providing personalized recommendations or advice.
Although it presents many opportunities, human-machine collaboration also brings its own set of challenges.
The main challenge is that AI technology is not perfect, and it can make mistakes or be biased, which means that humans need to constantly monitor the output of machines to ensure that it is accurate and unbiased. In addition, AI has certain limitations when it comes to understanding context, which makes it difficult for machines to interpret data in more complex situations.
Another challenge is that human-machine collaboration can be very demanding because both parties must stay on top of their game to achieve results effectively. This means there needs to be a lot of communication between humans and machines and a good understanding of each other’s strengths and weaknesses to work together successfully.
Humans can adapt and apply their skills to various situations, making quick decisions and learning new skills or strategies. On the other hand, robots require continuous programming to reach the same level of adaptability. This makes them less versatile than humans, who can use their skills and abilities in different settings and contexts.
Human-machine collaboration is becoming increasingly commonplace. We rely on our machines to help us with mundane tasks, automate processes, and even create content. However, while this technology offers great potential for efficiency, several challenges must be addressed before effective human-machine collaboration can be achieved.
For example, given their increasing role in decision-making, how can we ensure that machines act responsibly? How do we ensure humans are not over-reliant on machines for critical decision-making tasks? These important questions need to be addressed before we can fully integrate human-machine collaboration into our daily lives.
Despite that, as machines become more capable and powerful, human-machine collaboration is set to evolve and revolutionize the way we work in the upcoming years. As the global use of AI in organizations is expected to grow exponentially between 2022 and 2030, with an estimated CAGR of 38.1%. Indeed, this new form of collaboration between people and machines will open up new possibilities for businesses to be more efficient, create better products and services, and provide better customer service.
Ultimately, combining the strengths of both humans and machines, such as cognitive skills, problem-solving capabilities, creativity, and intuition, can give rise to smarter solutions. Together, humans and machines can create a more efficient, productive, and better future for all industries involved.
Imagine walking into a retail store where AI-powered screens customize product recommendations based on your preferences and purchase history. Or where you can simply check out with a scan of your phone without waiting in line. This may sound like a far-fetched future vision, but AI and automation are already transforming the customer experience in retail and online.
This article will explore how cutting-edge AI technologies improve customer experience in retail and e-commerce and how business owners can capitalize on their benefits.
Using the massive amounts of customer data, they collect from online purchases, browsing history, preferences, and demographics, AI systems can identify patterns and determine what customers most likely want or need. These AI algorithms can then generate tailored recommendations of products, offers, discounts, and content for each customer in real time.
These technologies can also monitor customer interactions and feedback in real-time to improve the personalization model for that individual over time continuously. As the AI system learns more about a shopper’s preferences, recommendations become more accurate and targeted.
AI can automate many repetitive and mundane customer service tasks, allowing employees to focus on more creative and value-added work. For instance, AI chatbots and virtual assistants can now handle basic queries and requests around the clock without human intervention. Self-service checkout solutions are also gaining traction.
These technologies mainly use computer vision and machine learning to allow customers to checkout, return items, scan inventory, and perform other functions independently. First, the computer vision recognizes items as customers scan them by matching products to the store’s database. Machine learning models are then trained on transaction data to improve accuracy and handle exceptions constantly.
AI systems can predict future customer needs, behaviors, and likely actions by analyzing customer data patterns. For example, AI can predict which products customers will most likely purchase next or when they need to re-purchase. This enables businesses to send targeted offers or recommendations at optimal times proactively.
On an operational level, AI predictive analytics can optimize inventory levels, staffing, and resource allocation based on forecasts of future demand and performance. It can even predict potential issues like out-of-stock items or delivery delays before they happen.
Visual search engines apply techniques like image recognition to analyze the important visual attributes of an image, such as color, pattern, shape, and texture. They then match these attributes to product inventory based on images and descriptions. As they analyze more customer queries and interactions, visual search engines continue to improve in accuracy and capabilities.
For customers, visual search improves the shopping experience by making it easier to find what they have in mind. They no longer have to describe the product in words or search through lengthy text-based results.
By utilizing AI to analyze customer data, businesses can better understand their customer’s needs and preferences to personalize the customer experience. This can improve customer engagement, loyalty, and satisfaction, as customers feel their needs are met.
Businesses can use AI to analyze customer data to identify patterns and trends to create targeted marketing campaigns and promotions. This can increase sales and revenue as customers are more likely to interact with personalized offers and promotions relevant to their needs and interests.
By automating repetitive tasks such as customer support and data analysis, businesses can free up time and resources to be redirected toward more strategic initiatives. AI-powered chatbots and VAs can also handle many customer inquiries simultaneously, reducing the need for human customer support agents and lowering labor costs.
While AI has tremendous potential to enhance the customer experience, its implementation in retail and e-commerce raises important privacy and ethical concerns. AI algorithms are designed to learn from data inputs, which means that the quality and integrity of the data used to train these algorithms are of utmost importance.
Hence, businesses must ensure that the data used to train AI algorithms are representative and unbiased and do not perpetuate discriminatory or unethical practices.
AI algorithms are only as good as the data used to train them, meaning businesses must have access to high-quality data and the expertise to manage and analyze it. Hence, they must ensure that AI solutions do not violate privacy laws or infringe on individuals’ rights. This includes ensuring that customer data is collected and used following relevant regulations and that individuals know and control how their data is used.
AI has the ability to transform customer experience in retail and e-commerce in ways that were previously unimaginable. As AI systems become smarter through machine learning and gain more insights from massive amounts of customer data, they can anticipate customers’ needs, wants, and behaviors with increasing accuracy. In this new retail paradigm powered by AI, customer experience might just become a reality instead of a goal.
Recently, the world has been witnessing a mindblowing digital transformation. Humans are empowering everything to their advantage; they’re even programming systems and integrations that can do their jobs and tasks. The artificial intelligence revolution is boundaryless and is now touching upon all business aspects, and the reasons for this expansion are many.
AI has major benefits and advantages that can make or break a business. In this article, we’ll cover the following:
No business can prosper and succeed without efficient interaction with clients; it’s the key to retaining customers, generating referrals, and strengthening a company’s culture. However, even though customer interaction is among the most basic tasks an employee can get engaged in, it’s time and energy-consuming. It can also be a talent waste for hard-working, dedicated employees.
AI helps improve customer service workers’ productivity by solving simple issues, saving time, and making their jobs easier. By that, reliable employees can focus only on complex and high-emotion scenarios. Automation technologies aren’t only a facilitator for clients and customer service employees, as the business can benefit financially from them. Companies can reduce costs significantly when the right AI technologies are implemented.
Cognitive overloads, lack of skills, and poor instruction are all reasons for human errors in the business field. And just like they say, to err is human. However, errors in the work field could have severe, deep repercussions, and sometimes regular breaks and training can’t cater to the work needs and still expose employees to the risk of committing mistakes.
On the other hand, AI is already programmed with the required work needs and demands no breaks, reducing error possibilities and ensuring everything is going according to the plan.
Sometimes, traveling in time to discover whether or not clients will welcome a new product is all that a business needs to decide whether or not to initiate a further step or step back. However, because our human brains are restricted to the now and the past, that isn’t possible. But luckily, artificial intelligence can reach the future and generate accurate predictions.
Artificial intelligence uses existing data and resources and predicts insights based on patterns. By that, companies will be enlightened about things they might haven’t considered before and consequently manage outcomes more professionally.
The recruiting process is a key player in any business’s success, and as important as this step is, it’s time-consuming and troubling, especially with an increased number of qualified and unqualified candidates. But with the help of artificial intelligence tools, redundant tasks are eliminated, hiring costs are reduced, and candidate assessments are objectified.
AI tools do 75% of the task by filtering candidates’ resumes, canceling the ones that don’t match the requirements, and keeping the ones that might be a good fit for the rule. The AI tool reduces the CV pool to 25%.
And from there on, the human element intervenes, and the hiring manager evaluates the given candidates’ options and settles for the best fit. This process, even though it needs a human element at the last stages, can save HRs a lot of time and inconvenience, especially those that receive tons of irrelevant and ineligible resumes.
Employee training is a critical part of any successful recruitment process. Employees, no matter how seasoned and experienced, won’t be familiar with a company’s work approaches and styles. As a result, fresh employees will need to undergo an intense training process so they level up to the company’s level of professionalism.
The data breach could mark the grave of a business; it can harm its reputation, result in unexpected expenses, and get it involved with legal penalties. And because hackers are always coming up with novel, complex ways to get to companies’ systems and computers, data security is any business’ top priority.
No IT team, no matter how dedicated, will be able to respond to stop a breach attack. That’s why AI solutions have emerged as a high-level protecting option to maintain a business’ security and privacy; these solutions can detect, prevent, and stop breaches at early stages.
Davinci IT Engineering is a technology firm established in Armenia that develops cutting-edge AI tools by leveraging Armenian ingenuity. It offers many services and solutions, including image classification, object detection and segmentation, tracking and counting, predictive analysis, and a recommendation system.
Davinci IT Engineering caters to many industries, including banking and finances, healthcare, and transportation.
On the 5th of October 2022, Davinci IT Engineering, represented by its CEO, participated in GIF, the Global Innovation Forum held in Armenia annually and organized by FAST Foundation. During GIF 2022, Company met with world leaders in the tech and innovation fields. Moreover, he used the opportunity to introduce Davinci’s innovative AI solutions that are skyrocketing in the logistics industry in the United States.
Recently Davinci developed an AI shipping cost calculator functioning in real-time to provide accurate quotations for auto-shipping clients based on machine learning and data extraction technologies. The tool was put into use in 2022 and caught attention rapidly with its results and accuracy.
Artificial intelligence integrations have become an important part of any business, saving tons of time and ensuring better service quality for all parties involved. However, one has to define their business needs and look for an IT engineering company accordingly.
In the few minutes that you are reading this article, I will tell you about a completely new type of artificial intelligence, I will name the design features and advantages, I will outline the immediate prospects and possible long-term consequences of the introduction of this technology into real life. Together we will touch the future.
This article is the fourth in a series on the nature of human intelligence and the future of artificial intelligence systems. In the previous article, “The secret of human intelligence,” we found out that human intelligence can work as a classical binary system-symbiosis, functioning due to the structural features of transmembrane proteins in ion channels of brain synapses.
At first glance, it might seem that this new and generally extraordinary neurophysiological concept is of interest only to doctors and biologists. But in fact, this idea from the world of neurophysiology opens the opportunity for us to create a very unusual artificial intelligence.
The existing systems of artificial intelligence, with all their features, have one thing in common: they are all built as single vertically controlled electronic complexes that operate using algorithms of varying complexity. Centralized control is an irresistible property of any man-made electronic computing system. We simply do not know how to build otherwise.
But what if we replicate the maneuver of nature and instead of the next modernization of the vertically integrated electronic system, we follow the path of unification to create a technological symbiosis of the human brain and the computer system?
If nature, the creation of our mind went along the path of symbiosis (combining the reflexive and intellectual components). And perhaps this is the shortest and most effective method of modernizing intelligent systems.
A new type of artificial intelligence will become a bioelectronic hybrid, in which a living human brain and a machine will work together in a dual complementary system. Both components will complement and reinforce each other, creating something completely new that neither nature nor designers of fully electronic systems have encountered before.
We will get acquainted with a new type of artificial intelligence of an individual type, built around a neurocomputer interface that directly connects the neurons of the human brain and a computer.
The heart of the system, or how will the neurocomputer interface work?
Despite the mesmerizing prospects of this direction, there have been only a few attempts in the world to create an interface connecting the human brain and a computer directly. One of the most famous was Elon Musk’s Neuralink. The weakness of these projects is that they follow the traditional surgical pathway and, as a result, fail to overcome two fundamental obstacles.
The first obstacle is the inaccuracy of individual interpretation of local foci of brain activity. Simply put, the brain of each of us is to a certain extent unique, if we talk about which groups of neurons are responsible for specific functions. But this is still half the trouble. Worse is that, thanks to plasticity, the detailed picture of brain activity is constantly changing.
The second, and truth be told, the main obstacle is the signal crossover point. Basically, this is where the artificial electronic signal becomes a biological nerve impulse and vice versa.
In the new artificial intelligence system, the transmitting and receiving parts of the neurocomputer interface will be completely separated and, in fact, will be two completely different communication mechanisms.
From biological tissue to computer
The receiving part (responsible for receiving a signal from biological tissue) will be a network of inactive marker objects (ultra-small nano-sized beacons integrated into living tissue) whose state will be remotely monitored by an active external component of the system (scanner). A marker object is a biologically neutral molecular structure (in the field of view of an external scanner), which changes its conformational state in the presence of a nearby weak electrical charge (a neuron at the stage of pulse generation). This technological technique will allow replacing the direct transmission of a signal from living neurons to a computer system for the transmission of information about the existence of such a signal. This will turn the receiving part of the neurocomputer interface into a non-invasive (non-traumatic) mechanism. In such a scheme, there is no need to do expensive surgical procedures, and molecular markers can be injected into the body using a simple intravenous injection.
From machine to biological tissue
The transmitting part (on the way from the computer to the biological tissue) will remotely transmit the signal only to synapses, and not to neurons, as they are trying to do now. The transmitting part of the interface will use marker objects (beacons of the receiving part) as points of orientation in space (addresses of neurons) and sources of feedback.
Interestingly, the signal that will only be transmitted to synapses must be of a non-electrical nature. This will allow us to generate an artificial signal (nerve impulse) in the neurons of the brain that is completely identical to the physiological one. As a result, the neurons of the human brain will experience stimulation of synaptic plasticity and, as a result, they themselves will actively participate in the formation of lines of dynamic interaction with the transmitting part of the neurocomputer interface. The brain tissue itself will build a connection with the transmitting structure of the interface.
In addition, to install such an interface, a person will not need to use the services of highly qualified medical personnel, which will make the system convenient for most users.
What does the movie Matrix have in common with the new AI
It is important to understand that the described scheme will allow controlled excitation and monitoring of the response of a single neuron. This discreteness means that the bandwidth of the interface will be enough to transmit directly to the human brain an artificial reality that is completely indistinguishable from physiological. You will be able not only to see, hear and feel the artificial reality but also actively move in it in the same way as in the real physical world.
Instead of words and letters – only a nervous impulse
Thanks to the new type of neurocomputer interface, the brain and the computer will be able to exchange data directly, without using intermediate communication protocols like sound commands or letter symbols. The brain and the computer will exchange information using a set of impulses of immediate meaning, without symbolic interpretation. As a result, interacting with a machine will feel more like working with intuition than interacting with an electronic device. Mutual adaptation of components (brain and machine) will take quite a long time (from several months to a year), but it will allow ignoring the language barrier and even literacy. Unlike modern computers, even a person who cannot read and write can use the new personal AI system.
Not just an interface
The neurocomputer interface, although the most important part of the complex, is not the only feature of the new artificial intelligence system. When we say a machine or an electronic component of symbiosis, we are talking about a software package that bears little resemblance to traditional artificial intelligence systems.
The core of the electronic part of the new system will be a rather unusual program that works based on direct streaming communication of templates (pre-prepared answers). Since this program will not actually do any calculations, the core of the system will be able to maintain tremendous performance that allows it to create a gigantic volume of data exchange required for synaptic discreteness. Other components of the software package will be enhanced deep learning utilities that do not directly participate in the dialogue with the biological tissue of the brain but provide service and individual adaptation of the core streaming program.
Artificial intelligence of a new type will be individual not only because it will be designed to work with one user, but also because it itself will be the result of direct adaptive interaction of the learning software package with the living brain of a particular person.
In fact, the machine, gradually adopting the behavioral habits of a particular person, will become its artificial reflex (unconscious) part, while the biological brain getting used to the machine (with the help of synaptic plasticity) will increasingly rely on the strength and capabilities of the computer system. In general, thanks to the imperceptible, but constant work of the neurocomputer interface, we will see how a biotechnological neurocomputer symbiosis is formed: a new type of artificial intelligence.
Creating artificial intelligence by combining fundamentally different elements (biological tissues and an electronic system), we will be able to achieve the maximum effect of emergence (the birth of new properties that are not inherent in the combined elements separately).
Biotechnological symbiosis will have properties unattainable for a biological brain and a computer system separately.
The human brain is very slow and frankly weak in terms of information processing, an intelligent mechanism, but the biological system has plasticity, creativity, and energy efficiency unattainable for electronic systems. Plus, the living brain is a very experienced tactician who knows very well how the reality of the surrounding three-dimensional space works.
On the other hand, computer systems not only process information faster than us, in fact, in terms of signal transmission speed, they exceed biological tissue by 3 million times! Add to this a digital memory capable of clearly and without failures to manipulate an unimaginable amount of digital data and the ability to easily enter direct communication with any technical device or the Internet.
All this suggests that combining the human brain and computer system into a single complex of artificial intelligence will not only increase their overall efficiency but will create a completely new unusual system: a new type of artificial intelligence.
By combining the brain and the machine, we will see how the real magic of new properties is born: the magic of emergence.
Why is such a system needed right now?
The main reason is the monstrous information explosion. Today, the amount of digital data on the Internet is doubling every 18 months. During the period from 1997 to 2002, mankind produced more information than in the entire previous history.
Now the same amount of data is generated in just a few months. Humanity as a consumer of information is catastrophically lagging itself, and this imbalance is growing literally every minute.
In fact, a person now needs no new information as a product but help in the conditional “digestion” of this product
It is this fact that opens a window of opportunity for new technology. Your personal artificial intelligence system will give you the ability to analyze the entire array of information available on the Internet, and not just what is on the first page of Google search results.
The growing volume of information that humanity creates will make it possible to create millions of IAIs (individual artificial intelligence systems) with ever-increasing performance.
The information consumption of a person equipped with his own AI will be thousands of times greater than the traditional information consumption based on biological (sign or acoustic) communication systems.
This article is not just about a new system, we are talking about a fundamentally different concept of artificial intelligence (AI). Your individual artificial intelligence system will know you, your personality, your requests, and preferences more accurately and clearly than you, it will be a part of you and at the same time your creation and extension: your digital shadow.
Man will become a living being of a new type
After 5-10 years of coexistence, the complementarity of AI components (brain and computer system) will reach such a level that several complex behavioral and communicative complexes (such as mechanical work or driving a car) can be carried out by the system reflexively (automatically). As a result, a person will be able to perform a huge number of routine actions without straining at all.
The economic business landscape is changing beyond recognition
A personal artificial intelligence system will replace a computer, smartphone, autopilot in a car, and much more. Any human skill and knowledge will become available for purchase or sale in a few minutes on the Internet. The painstaking and exhausting training we are accustomed to will gradually become unnecessary. Most disabilities will lose their limiting component.
Describing the properties of the new system, I understand that many people find it difficult to believe in the reality of such a technology. But in fact, behind this concept is a huge amount of scientific work done at the end of the last century. Over the past two decades, I have been able to piece together all the details of this extremely complex interdisciplinary project and, based on new data, find solutions for previously unrealizable structural elements.
Source: TechTalks
Meta has unveiled a new AI model called NLLB-200 that can translate 200 languages and improves quality by an average of 44 percent.
Translation apps have been fairly adept at the most popular languages for some time. Even when they don’t offer a perfect translation, it’s normally close enough for the native speaker to understand.
However, there are hundreds of millions of people in regions with many languages – like Africa and Asia – that still suffer from poor translation services.
In a press release, Meta wrote:
“To help people connect better today and be part of the metaverse of tomorrow, our AI researchers created No Language Left Behind (NLLB), an effort to develop high-quality machine translation capabilities for most of the world’s languages.
Today, we’re announcing an important breakthrough in NLLB: We’ve built a single AI model called NLLB-200, which translates 200 different languages with results far more accurate than what previous technology could accomplish.”
The metaverse aims to be borderless. To enable that, translation services will have to quickly offer accurate translations.
“As the metaverse begins to take shape, the ability to build technologies that work well in a wider range of languages will help to democratise access to immersive experiences in virtual worlds,” the company explained.
According to Meta, NLLB-200 scored 44 percent higher in the “quality” of translations compared to previous AI research. For some African and Indian-based languages, NLLB-200’s translations were more than 70 percent more accurate.
Meta created a dataset called FLORES-200 to evaluate and improve NLLB-200. The dataset enables researchers to assess FLORES-200’s performance “in 40,000 different language directions.”
Both NLLB-200 and FLORES-200 are being opened to developers to help build on Meta’s work and improve their own translation tools.
Meta has a pool of up to $200,000 in grants for researchers and nonprofit organisations that wish to use NLLB-200 for impactful uses focused on sustainability, food security, gender-based violence, education, or other areas that support UN Sustainable Development Goals.
However, not everyone is fully convinced by Meta’s latest breakthrough.
“It’s worth bearing in mind, despite the hype, that these models are not the cure-all that they may first appear. The models that Meta uses are massive, unwieldy beasts. So, when you get into the minutiae of individualised use-cases, they can easily find themselves out of their depth – overgeneralised and incapable of performing the specific tasks required of them,” commented Victor Botev, CTO at Iris.ai.
“Another point to note is that the validity of these measurements has yet to be scientifically proven and verified by their peers. The datasets for different languages are too small, as shown by the challenge in creating them in the first place, and the metric they’re using, BLEU, is not particularly applicable.”
Source: AINews
Computers can be trained to better detect distant nuclear detonations, chemical blasts and volcano eruptions by learning from artificial explosion signals, according to a new method devised by a University of Alaska Fairbanks scientist.
The work, led by UAF Geophysical Institute postdoctoral researcher Alex Witsil, was published recently in the journal Geophysical Research Letters.
Witsil, at the Geophysical Institute’s Wilson Alaska Technical Center, and colleagues created a library of synthetic infrasound explosion signals to train computers in recognizing the source of an infrasound signal. Infrasound is at a frequency too low to be heard by humans and travels farther than high-frequency audible waves.
“We used modeling software to generate 28,000 synthetic infrasound signals, which, though generated in a computer, could hypothetically be recorded by infrasound microphones deployed hundreds of kilometers from a large explosion,” Witsil said.
The artificial signals reflect variations in atmospheric conditions, which can alter an explosion’s signal regionally or globally as the sound waves propagate. Those changes can make it difficult to detect an explosion’s origin and type from a great distance.
Why create artificial sounds of explosions rather than use real-world examples? Because explosions haven’t occurred at every location on the planet and the atmosphere constantly changes, there aren’t enough real-world examples to train generalized machine-learning detection algorithms.
“We decided to use synthetics because we can model a number of different types of atmospheres through which signals can propagate,” Witsil said. “So even though we don’t have access to any explosions that happened in North Carolina, for example, I can use my computer to model North Carolina explosions and build a machine-learning algorithm to detect explosion signals there.”
Today, detection algorithms generally rely on infrasound arrays consisting of multiple microphones close to each other. For example, the international Comprehensive Test Ban Treaty Organization, which monitors nuclear explosions, has infrasound arrays deployed worldwide.
“That’s expensive, it’s hard to maintain, and a lot more things can break,” Witsil said.
Witsil’s method improves detection by making use of hundreds of single-element infrasound microphones already in place around the world. That makes detection more cost-effective.
The machine-learning method broadens the usefulness of single-element infrasound microphones by making them capable of detecting more subtle explosion signals in near real-time. Single-element microphones currently are useful only for retroactively analyzing known and typically high-amplitude signals, as they did with January’s massive eruption of the Tonga volcano.
Witsil’s method could be deployed in an operational setting for national defense or natural hazards mitigation.
Source: TechXplore
Machine learning is an exciting field of study and one that impacts and will continue to impact our lives as strongly as other technologies have. As such, you can be sure that it will generate a lot of research over the next decade, which makes it worthwhile to follow some promising ML blogs. I’ve picked out some of my personal favorites for data scientists, researchers, and enthusiasts. From researchers to students, industry experts, and machine learning (ML) enthusiasts — keeping up with the best and the latest machine learning research is a matter of finding reliable sources of scientific work.
While blogs usually update in a more informal and conversational style, we have found that the sources in this list are accurate, resourceful, and reliable sources of machine learning research. Fit for all of those interested in learning more about the scientific field of ML, and while there are several machine learning blogs out there, the scientific community is publishing a lot of research lately and it can be hard keeping up with the latest developments. Plus, it’s not always clear which blogs are worth following.
The goal of this post is to share with you some of the best blogs on machine learning. These blogs might be your best resource for reading the latest on machine learning, or they may keep you updated with what’s going on in this exciting field, and please know that the blogs listed below are by no means ranked or in a particular order. They are all incredible sources of machine learning research. Please let us know in the comments or by emailing us if you know of any other reliable blog sources in machine learning.