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]]>Earthquakes occur from movements in the Earth’s crust. These movements happen where there are active volcanoes, and along fault lines formed by the planet’s tectonic plates.
We think we live on solid ground, but we don’t. The Earth’s crust floats on a hot, viscous-solid but ductile layer called the mantle. The mantle stretches from 35 kilometres below continents, less below oceans, to 2,900 kilometres in depth. It forms more than two-thirds of Earth’s total mass. The upper part of the mantle is more plastic in consistency and moves. These currents are driven by thermal convection from the residual heat left over after Earth formed 4.5 billion years ago. The crust where we live is affected by the convection currents that lie beneath. The convection has produced the current condition of the Earth’s crust, which is broken into 7 major plates that underlie the continents, 7 smaller plates and dozens of microplates. By current accounting, the total number of plates exceeds 100.
From my teenage years, plate tectonics has interested me. As head of the Geography Club in my high school, I contacted John Tuzo Wilson, the Canadian geophysicist and principal architect of plate tectonics, to come speak to the students. Wilson was teaching at the University of Toronto and agreed to a speaking date. We packed the school auditorium, both students and the entire high school faculty. I kept in touch with him after.
It was no surprise when I chose to study geology and geophysics when I graduated. Studying ocean tectonic plates was going to be my specialty after I read Wilson’s theory of how the Hawaiian Islands chain formed. Wilson theorized that the entire group formed as the Pacific plate under them moved over a stationary mantle hotspot, causing volcanic eruptions. The Big Island today sits on top of that hotspot. (I might have followed in Wilson’s footsteps if not sideswiped by an accident that sidelined me for nearly two years.)
Today, we understand Earth’s drifting surface of plates using the Wilson Cycle, which describes the mechanism popularly known as Continental Drift. The consequences of plates in motion lead to separations, joinings, scraping, uplifting and subduction.
The visual evidence stares us in the face when we look at a globe. The west coast of Africa looks like it could fit into the east coast of South America as if the two were jigsaw pieces.
The movement is geologically slow, millimetres annually, until it suddenly isn’t. That’s when the Earth shakes.
Two earthquakes struck 39 seconds apart in northern Venezuela last month. Geologists described the two events as a rare “seismic doublet.” The quakes registered 7.2 and 7.5 on the Richter Scale, the method used to calculate earthquake intensity. It is logarithmic, so a difference of 0.3 translates to twice the ground motion and almost three times the energy released from the first to the second quake.
The close timing of the two has proven deadly for thousands of Venezuelans trapped under collapsed buildings, roadways, bridges, and other infrastructure. More than 3,500 have died, over 16,000 have been injured, and there remain tens of thousands unaccounted for.
The tectonic plates that produced the quake are one underlying the Caribbean Sea, and the other under South America. The Caribbean Plate, during normal periods, moves eastward approximately 2 centimetres (less than an inch) per year. The quakes caused an estimated 2 to 3 metres (approximately 6.5 to 9.8 feet) eastward shift for the Caribbean Plate, roughly a century of motion in 39 seconds.
Predicting earthquakes until now has proven to be difficult. Early warning signs can be detected by seismometers, accelerometers, tiltmeters and strainmeters installed on the surface or underground. GPS, radar and gravity measuring satellites overhead today can also detect surface movements. Some scientists observe changes in animal behaviour that precede earthquakes. Apparently, we humans have lost that ability. Other than what I have just described, our current ability to predict seismic doublets or single earthquakes is more pseudoscience than real.
That’s where AI can help. Researchers at the GFZ Helmholtz Center for Geosciences in Potsdam, Germany, have been building a historic seismic activity record of earthquakes.
They unleashed machine learning (ML) algorithms to study the record without including human concepts and labels. The ML, when exposed to this unlabelled data stream, worked its way through a catalogue of past events without direction. With this unsupervised learning, it produced findings that identified subtle “earthquake preparatory phases” not detected by the methods and instrumentation used by seismologists and geologists today.
The research appeared in the journal Nature Communications in May of this year. The authors conclude “that the method is capable of identifying preparatory phases (when present), showing potential for operational earthquake forecasting.”
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]]>The post Satellite Monitoring Revolutionizes 21st Century Agricultural Practices appeared first on Techso Mogro.
]]>Kateryna describes how remote monitoring using satellites is revolutionizing how farmers combat crop infestations and diseases through the analysis of high-resolution images and advanced data analytics. I’ll let her explain the rest. As always your comments are welcomed.
Pest and disease management is crucial in agriculture, protecting crops from seen and unseen threats that devastate harvests and livelihoods. Traditionally, farmers have relied on their experience and natural remedies. Today, geospatial data availability and analytics have transformed agricultural practices using tools like satellite imagery, drones, and a form of artificial intelligence called machine learning to detect pest and disease crop infestations allowing for early and precise interventions. Tools include smart sensors deployed to monitor soil and plant health in real time and data analytics to offer insights into effective pest control. Biotechnological advances using genetic modifications are producing disease-resistant crops and reducing the need for chemical pesticides.
Although satellites may seem far away and incapable of spotting something as small as an insect, other pests and early stages of disease, they are proving to be quite effective in these tasks.
Two technological advances, one an Earth observation instrument and the second, EOS Data Analytics when combined represent the current state-of-the-art to help farms monitor different crop and soil parameters from advanced remote satellite imaging and data collection.
Traditional methods while rooted in time-honoured wisdom and experience face many limitations and challenges in today’s complex agricultural landscape.
One of the primary issues is the unpredictability of pest and disease outbreaks. Growers often rely on manual inspections and past experiences, which can be subjective and inconsistent. This reactive approach means that infestations are often identified only after they have caused damage that is already impossible to avert completely.
Another limitation is the reliance on chemical inputs. Although they can be beneficial in the short term, these substances cause detrimental effects on the environment, including soil degradation, water contamination, and harm to beneficial insects and wildlife.
Another issue to consider is pest resistance to chemical treatments over time, which means even stronger hence more harmful analogues need to be applied with negative environmental consequences.
The labour-intensive nature of manual monitoring and intervention requires substantial time and effort which can be particularly burdensome for small-scale farm operations. Furthermore, these methods often lack precision and lead to the over-application or under-application of treatments.
Climate change also doesn’t work in our favour here. Warmer temperatures and changing precipitation patterns create better conditions for pests and diseases to thrive often in areas where previously they were of no concern.
Given these limitations, the need for innovative solutions becomes clear. Precision agriculture tools such as satellite imagery provide farms with real-time data and early warnings of potential threats. Machine learning and data analytics enable more accurate predictions and tailored interventions reducing reliance on chemicals.
Satellite technology with its far-reaching eyes offers growers a way to manage fields in ways that no other methods can. By analyzing high-resolution images farmers can see early indications of changes in vegetation patterns, colour and growth no matter the weather conditions and distance. The application of predictive analytics along with these images gives farmers the tools needed to make smart decisions.
Satellites identify signs of disease or pest outbreaks utilizing vegetation indices like NDVI, NDMI, ReCl, and NDRE. These track the state of health of plants at different stages of growth.
EOSDA Crop Monitoring from EOS Data Analytics leverages these indices to provide real-time insights into crop health. A crop monitoring system can assign scouting tasks to farmers based on satellite data. The system can confirm crop damage, identify specific diseases (biotic or abiotic), and communicate detailed descriptions to agronomists and farmers for timely interventions.
Using GPS coordinates a crop monitoring system can precisely target an area for intervention rather than address the problem as field-wide.
The predictive analytics in the Disease Risk feature of EOSDA Crop Monitoring spots nascent pest infestations and diseases before they become a larger problem. The analytics can assess risk levels across different zones allowing farmers to prioritize and address the most endangered crops.
A Data Manager feature provides data analysis comparisons to make accurate yield assessments during harvesting. It produces maps that highlight areas of varying productivity and can help identify and manage low-productivity plots resulting from biotic pathogens.
EOSDA Crop Monitoring also offers data on soil moisture levels, temperature extremes, and weather conditions to ensure that farmers can manage both biotic and abiotic factors to safeguard their yields.
Using real-time remote sensing data gives farmers the means to make smarter decisions when using pesticides to target affected crops. Detailed satellite imagery can identify exactly where these infestations are happening allowing for precision rather than blanket application of chemicals and pesticides. Cutting down on chemical and pesticide use lowers costs and reduces environmental risk.
Satellite tracking of soil health and moisture levels means farmers can use less of both more effectively ensuring these resources get applied where needed most to promote sustainable farming practices.
Expect satellite technology to continue to play a critical role in precision agriculture. Biotechnological innovation is also paving the way for safer pest control methods. A good example is the Wolbachia project which breeds mosquitoes that can’t transmit diseases. We are already seeing the use of biological weather stations to identify and track pest species and diseases providing valuable data to predict and manage outbreaks. At the same as in the Wolbachia project, we are using the science of genetic modification to deal with these invasive threats.
The Internet of Things is being deployed today to support agriculture. It is revolutionizing pest monitoring using millions of sensors to collect environmental, optical, and sound data. This real-time information enables early detection and facilitates targeted interventions.
Big Data analytics combine information from various sources including pest biology, weather patterns, and control activities to optimize strategies and provide holistic insights into pest dynamics. AI is enhancing analytics tools by adding the capacity to offer recommendations or even apply autonomous decision-making to a perceived threat.
Robotics and drones are bringing precision to pest management enabling their detection and control regardless of location. Drones and field robots can precisely target the use of pesticides or biological agents minimizing chemical use and environmental harm, and they are only getting more sophisticated and smarter.
Augmented reality and virtual reality are playing a part as well. Virtual reality can be used to train farmers. Augmented reality can give farmers a heads-up view of all the data and imaging information to help make decisions about where the problems lie and best practice solutions.
All of these advancements are creating more sustainable, efficient, and resilient agricultural practices that can meet the challenges of pest and disease management here in the 21st century.
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]]>The post Using AI to Find ET appeared first on Techso Mogro.
]]>How do you pick candidates from this growing number of planets that should be studied in greater depth in the search for extraterrestrial (ET) life? The JWST can analyze the atmospheres of rocky and gas giant planets circling their parent stars. It can detect the elements that could reveal biosignatures. Machine learning, however, can be used to sift through mountains of data to determine the biogenicity of organic chemistry detected in physical samples or data collected from the JWST and other tools that study exoplanets.
Machine learning is a form of artificial intelligence (AI). An algorithm is created and designed to analyze data in search of specific information. Machine learning works much faster than any human when presented with mountains of unstructured and structured information. And as a form of AI, it just seems to be an appropriate tool to use to ferret out ET.
The latest machine learning advance related to studying Mars and other planetary bodies in a search for past and present ETs is being referred to as “the Holy Grail.” Led by scientists from
A team of scientists led by the Carnegie Institution for Science, Purdue University, John Hopkins University, the Tokyo Institute of Technology and the Blue Marble Space Institute for Science have developed a test to determine if past or present life exists on other planets. It is deemed to be 90% accurate in being able to distinguish biology both past and present from sampling data. In other words, it can find ET. The results have been published in the last month in the Proceedings of the National Academy of Sciences (PNAS).
Stated Robert Hazen of the Carnegie Institution for Science, in Washington, DC, “This routine analytical method has the potential to revolutionize the search for extraterrestrial life and deepen our understanding of both the origin and chemistry of the earliest life on Earth.” He continued, “It opens the way to using smart sensors on robotic spacecraft, landers and rovers to search for signs of life before the samples return to Earth.”
The test uses pyrolysis-gas chromatography combined with mass spectrometry to do sampling. It was trained using living cells, fossil organic material, laboratory-synthesized organic compounds, and carbonaceous meteorites and was able to identify “biogenicity” in present-day and ancient geological samples with ~90% accuracy.
What is biogenicity? Any living thing both past and present when interacting with its surrounding environment leaves telltale signatures. That means this new test can be used to study samples already collected by both the Mars Curiosity and Perseverance rovers.
The researchers describe three big takeaways related to biogenicity.
The AI developed for detecting the presence of past or present life searches for subtle differences in molecular patterns to conclude whether something is or was alive. The training involved exposing the AI to 134 known abiotic or biotic Earth samples.
The proof in the pudding will come when the AI gets to sample data from Mars or other sites. Robert Hazen asks some interesting questions such as “If AI can easily distinguish biotic from abiotic, as well as modern from ancient life, then what other insights might we gain? For example, could we tease out whether an ancient fossil cell had a nucleus, or was photosynthetic?… Could it analyze charred remains and discriminate different kinds of wood from an archeological site? It’s as if we are just dipping our toes in the water of a vast ocean of possibilities.”
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]]>So many today fear where technologies like artificial intelligence (AI) and robotics are heading. Some even see mass unemployment should these technologies triumph replacing the human workforce. Others welcome the idea of using AI and robotics to lessen the burden of work leading to four-day work weeks. Others see the technologies as helping them to become unshackled from routine releasing their more creative selves.
But I will leave it to Katie to give you her insights into the ongoing technology revolution.
Technology changes business causing reactions ranging from amazement to fear. Businesses become more efficient, accurate, and capable of delivering products and services previous generations would not have imagined. At the same time, many fear losing jobs as they get replaced by machines.
Managing and implementing technology properly helps organizations reap the benefits while avoiding pitfalls. When done right, technological innovations create competitive advantages and increase revenue and profitability.
So what are these technologies that are revolutionizing business?
Ever since ChatGPT was released at the end of 2022, it has been hard to be online and not see something about the impact of this particular type of AI as well as others.
Speaking of ChatGPT, it is a generative AI large language model (LLM). Today it and competitors are creating written content and images, taking on online customer service queries, populating help desks and more. Companies are flocking to it to try and see what it can do for them. ChatGPT and other AI software are analyzing data at scale and starting to impact almost any area of business. Where is it all going?
As generative AI gets better at responding to humans and sounding like one, there will be pressure for writers and other creatives to add value. Organizations will be asked to stop doing things as they have been done in the past. More will focus on creating an innovation culture using AI to help with outside-the-box thinking. With the world only recently having shaken off COVID-19, new existential challenges are current and on the horizon. There is no time for businesses to be complacent.
AI analytics will help companies to optimize operations and will become increasingly granular, able to make suggestions in real time to employees wherever they are in the organization and whatever they are doing. Workers will come to rely on AI insights just as much as they might a skilled coworker. The goal will be to improve performance across the board.
We’ve come a long way from the Ford-style production lines of the early 1900s. Nowadays, robotic arms are used to manufacture complex pieces of machinery too large and too complex for human beings to assemble by themselves. Increasingly, these machines are autonomous, meaning no human operators are needed.
Robots are integrating AI and a particular form of it, machine learning, making them deployable in an increasing number of scenarios. And robots are getting smaller and more agile. Many of Amazon’s warehouse tasks are now being carried out by robots working side-by-side with human handlers.
Of course, robots are not always physical. Today, we have automated chatbots beginning to provide online customer service. These software robots learn and adapt as they are exposed to more data. Soon, help desks and online chat may require very little input from human handlers.
When someone talks about virtual reality (VR), many think immediately about the headsets worn by VR gamers. VR, however, isn’t just for playing games and is already being used in businesses for entirely different purposes such as digital twin workspaces created in the metaverse. VR is being incorporated into organizations to bring together remote and hybrid teams and improve collaboration. A company’s digital twin that runs in parallel to the bricks-and-mortar business can serve many purposes. It can expose gaps in operations that can be closed first in the digital and then in the real world. It can bring people from remote physical addresses to interact within the digital space.
VR can play a critical role in training particularly where there is a degree of physical risk or where tasks in the real world for training purposes would be very expensive. This is particularly true for companies supporting the military. A good example is an American technology-focused defence and security engineering company called Parsons which uses VR to train employees on hazard identification strategies without exposing them or the environment to the real thing.
According to Verizon, organizations such as Meta, Microsoft, and Amazon have created new-hire training using VR to help educate people about the culture and requirements of their workplaces and to allow new coworkers to meet in virtual company environments.
While new technologies offer many benefits, organizations need to plan their implementation. Otherwise, there can be significant setbacks that can hurt a company’s workforce, revenue, and profitability.
For example, many industries already have legacy technologies in place that can be challenging to replace all at once. Business leaders need to have patience and work incrementally to minimize downtime, workflow disruptions, and security concerns.
It’s also important not to lay off employees and replace them with technology that isn’t capable of doing their full jobs, which is something American media company Gannett found out the hard way when the newspaper chain tried to use an AI tool to write high school sports dispatches. While AI technology can do many things that humans can do, without the latter’s oversight mistakes will happen.
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]]>The post If You Fear Artificial Intelligence Taking Over the World Remember Who Controls the Plug appeared first on Techso Mogro.
]]>So the focus of our concerns shouldn’t be whether computer intelligence can eventually dominate but rather should be on the companies that are driving the AI boat. Alphabet (Google), Meta, Microsoft (OpenAI), ByteDance and other players are into AI and large language models in a big way. All are churning out chatbots that emulate the recently launched ChatGPT. Elon Musk with X.AI has joined in the pursuit with a claim that the AI his company produces will be ethical.
Sounding the alarm about AI consciousness and super smarts is not where our focus should lie. Instead, we need to be regulating AI’s purpose and usage. There needs to be an AI rulebook.
Recently, the European Union (EU) launched an initiative to legislate guardrails to govern AI development and usage within its member states. It provides a harmonizing of rules with “socially and environmentally beneficial outcomes” for humanity and the planet.
The rules cover the storage, sorting, and manipulating of data. They focus on safe, lawful, and trustworthy development and usage that respects existing laws and fundamental human rights.
The rules methodology defines risks posed to human health and safety and obligates AI developers and users to follow and enforce its regulations throughout product lifecycles with enforcement to be done within all member states.
The EU defines high-risk AI systems and, for these lays out specific corporate human oversight responsibilities that cover codes of conduct, and reporting requirements and obligations including public access to source code and confidential information from AI developers and users.
The use of AI in the EU rulebook states that it cannot be incompatible with fundamental rights enshrined in the EU Charter that include: the right to human dignity, respect for private life and protection of personal data, non-discrimination and equality between women and men, freedom of expression, freedom of assembly, fair trial, the presumption of innocence and the right to defence, as well as the general principle of good administration. In addition, the rulebook ensures that no AI can interfere with special groups such as workers’ rights to fair and just working conditions, consumer protection, children’s rights, and the rights of those with disabilities. The rulebook also states that any AI development and use must respect the environment concerning the health and safety of people and nature.
AI deployment must include pre-testing to ensure compliance with all of the above and to minimize any risks that could come from datasets that lead to erroneous or biased AI-assisted decisions in critical areas such as education and training, employment, law enforcement, the judiciary, and social rights. Should infringements of any of the above fundamental rights, a method of effective transparent and traceable redress will govern the developers and users of all AI systems.
What is the definition of high-risk AI systems? According to the EU which lists “a limited number of AI systems whose risks have already materialized or are likely to materialize in the near future,” it includes the following:
Anytime a person interacts with an AI, the EU rulebook requires the latter to identify itself as such. If images, audio or video, are manipulated using AI the creator of the application is obligated to disclose this to viewers and listeners. This includes the use of AI by any law enforcement agency.
And finally, the EU rulebook obligates its member states to create within their boundaries a competent supervisory authority, and to share expertise with all EU members.
In the last week, the White House in Washington, DC, summoned the senior executives of Alphabet (Google), Microsoft, OpenAI, and Anthropic to discuss where their AI software development is going. The meeting was held by Vice President, Kamala Harris with President Biden dropping in during it to deliver a message which stated, “What you’re doing has enormous potential and enormous danger.”
But if you go back to the beginning of this article, humans still hold the power of the plug when it comes to how and when, and when not to use AI.
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]]>Q: The process of approvals for new breakthrough medications, vaccines, and treatments seems onerous when you think of the time it has taken for laboratory breakthroughs to turn into clinical trials, and get final approvals from national health agencies around the globe for public use. How is BenchSci cutting through all of that to make new life-saving discoveries accessible to the general public faster?
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]]>The first involves the use of AI to identify areas within human organs that surgeons can safely dissect before operating on a patient. The second is the ability for AI to predict if memory issues in patients will develop into dementia within two years. And the third is the use of AI to analyze eye scans done during routine visits to an optician to identify patients at high risk for a heart attack.
In Toronto, Dr. Amin Madani, a general surgeon at the University Health Network, has developed a promising use for AI to improve surgical procedures and patient outcomes. Using deep learning, a subset of machine learning, Dr. Madani has trained an AI model by studying thousands of surgical procedure images involving dissection of livers, gallbladder, and other organs to identify safe and dangerous areas within organs’ anatomical structures to provide guidance during operations. The AI was able to score go and no-go zones for intervention with considerable accuracy.
In an article posted to CBC News in December of last year, a surgical instructor from Case Western Reserve in Cleveland, who collaborated with Dr. Madani is quoted stating, “The hope is to…bring a second pair of eyes into the operating room, in this case, machine eyes, to ensure the surgeon is seeing whey they think they’re seeing.”
How does this deep learning AI work? When a surgeon uses laparoscopic techniques to do gallbladder and other organ procedures. a camera gets inserted through the slit and projects images of the surgical field on a screen. What the AI does is project colours onto the images the surgeon sees. A green-coloured area of the surgical field is safe to cut. Coloured areas that are red are not.
The progress being made by Dr. Madani and his collaborators was first published in 2020 in the Annals of Surgery journal. The initial organ of choice has been the gallbladder since it is one of the most common operations and relatively easy to fully document with imaging from thousands of successful procedures. Based on the initial success the plans are to expand its use for other organs and a wider range of surgical interventions.
Machine learning (ML) is proving to be an effective AI tool in helping to identify false diagnoses of dementia from those who suffer from it in its most progressive form leading to an Alzheimer’s Disease diagnosis.
A study done at the University of Exeter in the United Kingdom shows that in looking at data from 15,307 individuals attending memory clinics, its AI tool with high accuracy predicted which of them would develop dementia within two years.
Clinical assessment tools exist for predicting dementia in patients over the longer term, but the challenge for clinicians working in this field is to assess patients who in the short term might be at high risk. Prior to the development of the ML tool, no clinical decision-making tool had previously been developed for identifying likely Alzheimer’s diagnoses within a window of one to two years.
How does this AI work? The ML tool studied the files of patients being followed between 2005 and 2015. All of the files the ML looked at were of patients who did not have dementia upon initial clinical examination. Many exhibited memory issues or had problems with other brain functions. But the ML tool was able to establish patterns within the patient record and identify the level of risk with much higher accuracy than human clinicians. It accurately diagnosed 92% of patients who developed Alzheimer’s within two years. It also accurately identified 80% of inconsistent diagnoses. In the study’s conclusions, it noted that the ML tool has the potential to reduce misdiagnoses by up to 84%.
The research lead who oversaw the study, Dr. David Llewellyn, at the University of Exeter, is quoted in an article appearing on the Biotechnology Community website, “We’re now able to teach computers to accurately predict who will go on to develop dementia within two years. We’re also excited to learn that our machine learning approach was able to identify patients who may have been misdiagnosed. This has the potential to reduce the guesswork in clinical practice and significantly improve the diagnostic pathway, helping families access the support they need as swiftly and as accurately as possible.”
A deep learning AI being developed at the University of Leeds in the United Kingdom has been trained to read retinal scans and identify patients who were likely to have a heart attack within a year. The study’s results appeared in the last week in the journal, Nature Machine Intelligence.
How does the AI tool work? Changes to blood vessels in the retina are indicators of vascular disease complications. This has been known by doctors for some time. But which patients go on to develop heart attacks within a short window of time remained an unknown. The deep learning AI analyzed retinal scans and cardiac scans from more than 5,000 patients. It was able to identify what changes it saw in the retina with changes in the heart. Once the patterning was learned the AI was able to estimate the pumping efficiency and size of the heart’s left ventricle which if enlarged is directly linked to increased risk of a heart disease. This information along with a review of the patients, age, and sex, led to a tool capable of accurately predicting 70 to 80% of the time if the patient was at short-term risk of a heart attack occurring within 12 months.
As a diagnostic tool, this allows doctors through routine and inexpensive eye screening examinations to intervene with early treatment to prevent cardiovascular disease leading to a major heart incident.
One of the researchers involved in the study, Professor Sven Plein at the university, is quoted in a press release stating, “The AI system is an excellent tool for unravelling the complex patterns that exist in nature, and that is what we have found – the intricate pattern of changes in the retina linked to changes in the heart.”
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]]>The post Is the Artificial Intelligence Classified as Machine Learning Becoming More Personable? appeared first on Techso Mogro.
]]>Machine learning is a technology that gives us language translation applications, word prediction when composing emails and texts, and suggestions on the order presentation within social media feeds. It is a technology used by many industries from healthcare where it can aid in medical diagnosis and interpretation of radiology images, as well as in the operation of autonomous vehicles.
Machine learning is a subcategory of artificial intelligence (AI), software tools that learn without explicitly relying on programming. Many companies deploying AI today are primarily using machine learning to help reduce labor costs and increase productivity.
How it differs from other AI is in its statistical focus. It studies large amounts of data and filters it through models focused on finding patterns. It processes mountains of data quickly and efficiently and is particularly good at analyzing data feeds from devices connected to the Internet in what is called the Internet of Things (IoT).
It can and does because of its ability to fit with so many applications. The recommendation algorithm that Facebook (now Meta) uses for its news feeds is an excellent example of applying machine learning to fit personal preferences. Meta recognizes when you stop reading a particular group of posts and news items. Its recommendation algorithm starts showing a wider range of content in the feed. So when you change your normal practice of reading a particular group of posts and switch to another it is already learning to feed you content from this new stream. But there is more to machine learning than recommendation algorithms.
Some companies and businesses use machine learning technology to collect varying information from analyzing different images. The technology helps businesses identify people and gather information about their character traits.
Facial recognition, seen as controversial, is a form of machine learning. This is a technology that can identify individuals and grant them access to a site. But it is also technology being used to pick individuals out of a crowd for immigration and law enforcement purposes.
One of machine learning’s greatest successes is in analyzing medical images. Machine learning is proving to be better and faster at detecting cancer and other diseases than radiologists and oncologists. It can examine vital patient information collected from laboratory specimens to determine diagnosis and treatment at speeds and in volumes not possible by diagnosticians. It can also review image databases and provide medical researchers with material for studies looking at causes and potential cures for conditions and diseases.
Today when you contact many companies through chat and online help, it’s not a human you are interacting with, but rather a machine language algorithm. Chatbots rely on machine learning to determine the language of communication, and the appropriate responses to a customer enquiry based on analyzing the language used in the chat, and building a knowledge base of a customer based on his or her responses, and on previous conversations and communication.
Machine learning technology is great at analyzing patterns. Banks and credit card companies find this capability very useful as customers buy and spend. Machine learning gets to know not only what you buy, but where you buy from. Changes to the normal pattern are noted and sometimes lead to a query from the bank or card company. This form of machine learning is great at catching not just transaction fraud, but also multiple attempts at log-ins to accounts that don’t fit your normal patterns.
Machine learning can leverage data and information to make decisions. It consistently learns through audio, speech, and language processing to adjust to becoming autonomous in its predictive abilities. The more data it sees, the more reliable is the feedback it provides. This is particularly useful for online marketers who use machine learning to analyze phrases, content, and keywords used by prospects to promote products and services to them. Marketers use the knowledge gained to personalize content for any customer increasing the likelihood of successful interactions and increased sales.
When a parent suggests you try an unfamiliar food, read a new book, or any other normal transaction, it is likely that you will follow those suggestions in your formative years. That’s because your parents likely know you better and know what you like and dislike. In that sense, machine learning is exactly like that parent.
Think of businesses trying to get to know you better. They don’t have the benefit of years of parenting to figure out what are your preferences. But machine learning gives them a shortcut to get there. The technology’s ability to gather information about you as a customer is why it can be a “pseudo” parent making suggestions on what you may want to purchase or try. Talk about being personable. It becomes hard to resist.
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]]>The post Can Transportation Innovation Contribute to an Environmentally Sustainable Future? appeared first on Techso Mogro.
]]>Technological innovation is instrumental in the planet’s move to a sustainable future. Nowhere is that more true than in the transportation sector. From railroads, to air, to vehicle ground transportation, new technology is enabling our mobility.
Tools like AI, information systems (IS), and machine learning are making this possible. How?
Imagine a train capable of operating on its own, avoiding collisions, and tracking passenger capacity all using onboard systems. This is exactly where trains are evolving in the 21st century. This form of ground transportation, almost two centuries old, is just one segment of the sector where AI is being deployed.
Its also turning up in all kinds of vehicles, including electric and hybrid cars and trucks. Since commercial vehicles account for a large portion of the sector, the increasing prevalence of AI systems in vehicles is set to make the world a cleaner and safer place. That’s because onboard AI:
Human error is a contributing factor in more than 90% of all traffic accidents. Any safety improvement, that takes human operations out of the equation, is seen as a plus. AI can analyze the driving environment, and assess dangers with a precision that a human driver cannot match.
Smart features are making for more fuel-efficient vehicles. Onboard applications track ideal GPS routes. They inform drivers about preventive maintenance when needed. They improve the efficiency of the vehicle during operations. All these make for greater sustainability just when most vehicle manufacturers in the world are transitioning their models to electric, hybrid, and hydrogen fuel cell models.
IS provides the foundation for how we collect and manage data. Without IS transportation and supply chain managers would be at a loss to track and maintain large vehicle fleets. That’s why transportation is a huge IS growth sector with global aviation alone expected to employ up to 99.1 million people over the next 20 years. The majority of these jobs involve overseeing information to optimize efficiency and achieve sustainability.
Since the air travel industry accounts for 860 million metric tons of carbon dioxide atmospheric pollution annually, the ability to reduce fuel usage and to optimize flights are a must for global sustainability. IS, AI and smart dashboards are making the transition possible.
Transparency means preventive maintenance, optimized routes, and lower emissions through the introduction of new technologies for air travel. Short-haul electric, fuel cell, hybrid, and hydrogen are all in the mix of technologies and innovation for air travel and transportation within the next decade.
Machine learning is a form of AI. It does exactly what the name implies. A machine learns based on the data models it is fed. Then, it learns through pattern recognition and begins to form intelligent conclusions. Over time machine learning improves its programming on its own. In manufacturing, machine learning is used to streamline efficiency and reduce waste. In transportation, it brings similar benefits.
With machine learning, full automation of vehicles is becoming a near reality. In other industry sectors, it is helping to power smart cities and curtail urban air pollution. One machine learning IBM project, Green Horizon, uses machine learning to track and predict pollution in urban environments.
All of the above technologies are game-changers for transportation. They will allow us to raise operational emissions standards in trucks and automobiles. And with the cost of renewable energy declining by 85% over the last decade, the ability for transportation to source its energy requirements for electric vehicles, and for green hydrogen to be used in fuel cells will become increasingly more affordable. That’s how technology will move the transportation sector towards a sustainable future. In an industry that is all about getting to destinations, you don’t want to be left behind.
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]]>AI gathers, models, and assesses data to produce the most algorithmically sound solution for a problem. As a result a business can rapidly develop better methods of doing just about anything.
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]]>The robots aren’t just coming, they are already here. And with artificial intelligence (AI) increasingly being used by industry at the enterprise level, businesses are paying attention to how AI can impact their processes.
Advancements in enterprise AI have been widespread impacting social, environmental, and financial processes. For a look at the current state of enterprise AI and how its changing business and outcomes read what follows.
AI opens up a world of new capabilities for business. The AI of today exploits the big data revolution and machine learning (ML). The convergence of these two is leading to hyper-automation, a technological trend that’s taking the world of business by storm.
What characterizes hyper-automation? It removes human labour from business processes by using machines to monitor, assess, and streamline practices for greater efficiency. Hyper-automation is creating breakthroughs in manufacturing, making it possible for industries to use robots from the design processes onward, streamlining parts production with AI insights, and reducing waste using tools like nesting software.
AI gathers, models, and assesses data to produce the most algorithmically sound solution for a problem. As a result, a business can rapidly develop better methods of doing just about anything.
One element of hyper-automation allows a business to create a digital twin of its processes. This is done using sensor-collected data which is then paired with a virtual model of equipment and workflows. The AI and ML software then produces insights and predicts outcomes using computational models.
As you might imagine, the impact on business processes using these tools can be revolutionary. But some of the outcomes may have negative consequences.
From science fiction films, AI often gets a bad rap. Fears of automation displacing human labour are common. But is automation actually replacing people or creating new opportunities for more fulfilling work?
Three factors need to be taken into account when gauging AI’s impact on business enterprise. These include social, environmental, and financial implications.
Enterprise AI is known for its ability to automate processes. This has two primary consequences, one good and one not so much.
First, there are time-saving features to highly automated business processes. In fact, 78% of business leaders believe AI automation frees up three hours of work daily. This translates into time that can then be applied to other areas of the business.
For example, freeing up human labour through automation has already seen beneficial social impacts in healthcare, where AI-assessments of CT scans help diagnose cancer better and faster than medical professionals. That’s because AI can evaluate thousands of scans and learn what is normal and abnormal using pattern recognition to become better at diagnosis. AI sees things that may be overlooked by a radiologist and help with triage to streamline treatment for those most at risk.
Second, what AI serves to improve may have a negative impact on jobs. Reports indicate that by 2025, 87 million jobs may be lost to automation and AI. The same reports however also note that new technologies will add 97 million jobs. I would say the jury is still out, therefore, when considering the negative consequences of AI when looking at job displacement.
AI is improving both the efficiency and productivity of businesses all over the world. This has potential environmental benefits. AI-assisted production and Computer Assisted Design (CAD) build parts with fewer resources. Research shows that AI can yield more sustainable and environmentally-friendly processes from the supply chain to manufacturing, distribution, consumption, recycling and reuse.
Financially, the impact of AI on business processes is positive across the board. From the automation benefits to the avenues for market growth, enterprise AI produces cost savings and efficiencies. That’s why there is a high demand for AI developers, set to grow by 22% over the next decade yielding applications for improved customer service, product development, and workflow monitoring. AI-powered innovations like chatbots and robotic process automation (RPA) represent investments today that are saving companies time and money.
As we see the potential of enterprise AI expand, its capabilities and benefits are growing. Through hyper-automation, there is little that machines cannot achieve. The resulting boosts in efficiency and productivity mean financial savings and a substantial boost to the business’s bottom line. It will be interesting to see if social and environmental impacts are equally as effective.
Time savings from AI implementation can mean a business workforce can focus more on real social and human issues. And AI focused on modelling and delivering sustainability can yield a greener world.
For the future of work, the effects of AI on business processes should lead to positive benefits for all three facets of business: the social, environmental, and financial.
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