Sounding the Alarm: Call for Americans to Oppose Biden Title IX Plan!
WASHINGTON / February 20, 2024 – A new poll of 1,600 persons reveals a majority of registered voters across the political spectrum now support state laws that would require children to wait until age 18 before they can receive transgender treatments: Republicans: 73%; Independents: 71%; and Democrats: 61%. (1)
The controversy came to light last week in New Hampshire where a new record was set in the girls’ high jump competition. Maelle Jacques succeeded in jumping 5’1”, breaking the previous female record by a full inch (2). But the athletic accomplishment was overshadowed by the fact that Jacques is a biological male who now identifies as transgender.
Similar reports have become commonplace for a range of concerns related to Title IX, the federal sex discrimination law: Due process for falsely accused male students (3), gender transitioning of underage minors (4), pronoun mandates (5), campus free speech (6), and more.
The uproar springs from a controversial 2022 Department of Education proposal to change the definition of sex to include “gender identity” (7). The Title IX plan has faced strong opposition across the country:
Numerous attorneys general and federal lawmakers issued statements of opposition (8).
25 Republican governors called on the Biden administration to withdraw its proposed changes to Title IX. (9)
Nearly 60 political candidates signed a Pledge to “Protect Schools, Children, and Families from the Federal Title IX Plan” (10)
In addition, 23 states have banned gender transitioning among children (11), 10 states outlawed pronoun mandates (12), and 23 states enacted laws to protect women’s sports from transgender athletes (13).
On February 2, the Department of Education forwarded its controversial regulation to the federal Office for Management and Budget for final approval.
SAVE is urging the American public to speak out in strong opposition to the Biden Title IX plan. We invite you to contact the Office for Management and Budget to politely express your concerns. For details how to schedule a meeting, visit: https://www.saveservices.org/2022-policy/abolish-doe/
The law would punish false accusers
with a minimum prison sentence equal
to any jail time served by the victim of
the false accusation. Such a statute might look like this:
18 Stat. § 101: False Accusations of sexual misconduct.
(a) Any person who makes any
utterance, publication or statement
that states or implies that another
person is guilty of rape or sexual
assault, knowing that the utterance,
publication or statement is false, or,
having reason to know that the
utterance, publication or statement is
false, shall be guilty of a Felony.
(b) This offense shall be charged as “False sexual reporting.”
(c) There shall be no immunity or
privileges asserted in response to a
charge under subsection (b).
(d) Any person who violates the
provisions of subsection (a) shall
serve a term in prison not less than
any amount of time, including pre
trial confinement that any falsely
accused served as a result of the false
accusation.
(e) Any person who violates the
provisions of subsections (a) or (b)
shall be strictly liable for any
damages the falsely accused victim
incurs as a result of the false
utterance, publication or statement.
(f) Any court entering judgment
against a false accuser under this
provision shall award full restitution
to any falsely accused, including
restitution or damages that arise or
become known after any hearing on
restitution. A victim of a false
accusation of rape or sexual assault
may re-open the issue of restitution in
any criminal proceeding under this
provision, at any time, to seek
additional compensation for
restitution of damages or injuries.
(g) There shall be no statute of
limitations for any charge brought
under this provision.
(h) Any person who violates the
provisions of subsection (a) or (b) of
this provision shall be required to
register as a sex offender under any
applicable state or federal laws.
(i) Punishment under this provision
shall be the minimum prison sentence
as specified in subsection (d) up to
and including twenty-five years in
prison, plus a fine of up to $500,000
or both.
Public Opposition Mounts As Media Accounts Spotlight Transgender Tragedies
February 14, 2024 – Abandoned by her birth-mother at an early age, Jennifer Kolstad had experienced a variety of mental health problems. After she began to identify as a boy at age 13, her parents cautioned that she was too young to make such a life-altering decision. When her parents refused to approve gender-transitioning procedures, the Montana CPS forcibly removed her from the family home (1).
Most Americans favor laws that protect adult transgender persons from discriminatory policies in jobs and housing (2). But driven by federal policy (3), transgender “horror-stories” such as the Montana case are serving to bolster public opposition to these practices.
A 2021 Gallup poll found that 62% of Americans believed that transgender athletes should be allowed to play only on teams that matched their birth sex. When the same poll was repeated in 2023, the percentage of Americans supporting such bans had increased to 69%. (4)
Five other national surveys conducted in 2023 reveal consistent opposition to transgender policies:
Deseret News/HarrisX (5): 55% of respondents support banning gender hormone therapy on minors, and 61% want to prohibit surgical interventions to change a child’s sex.
Summit Ministries (6): 77% of voters believe that allowing males who identify as female to compete against biological women in college female sports has been harmful to women’s sports.
CRC Research and Parents Defending Education (7): 74% of registered voters believe that schools should not be allowed to help students change their gender identity without parental consent.
Scripps News/You Gov (8):
54% of Americans support a federal ban on transgender females competing in school athletics.
44% say they want “laws that would restrict and, or ban transgender care for minors, even with parents’ consent,” while 34% oppose such measures.
KFF/Washington Post (9):
57% of American adults believe a person’s gender is based on their biological sex at birth.
67% do not think that biological males should be allowed to compete in women’s sports competitions at the high school level.
65% don’t believe that biological males should be allowed to compete in women’s athletics in college or professionally.
77% of American adults believe it is inappropriate for teachers to discuss their transgender identities with students in kindergarten to third grade.
Since 2022, 15 national polls have been conducted on a variety of transgender-related issues, revealing a national consensus against so-called “gender affirming” policies for children and youth (10). Lawmakers who support such policies are likely to face a negative response from voters during the upcoming November elections.
Public Opinion Polls Reveal Growing Public Opposition to Policies Driven by ‘Gender Agenda’
SAVE
February 12, 2024. Updated in February and June, 2024.
Fifteen national polls were conducted in 2022 and 2023 to gauge public opinion about a variety of Gender Agenda issues, including parental rights, women’s sports, classroom instruction, and gender transitioning among underage children and youth.
Two polls were conducted in January, 2024:
A poll of 1,600 registered voters revealed a majority of persons across the political spectrum now support state laws that would require children to wait until age 18 before they can receive transgender treatments: Republicans: 73%; Independents: 71%; and Democrats: 61%.
A NORC poll asked 1,624 adults to weigh in on whether trans athletes of both sexes should be permitted to participate in sports leagues that correspond to their preferred gender identity instead of their biological sex. Two-thirds of the respondents said it should be permitted “never” or “in rare cases.”
All of the polls were representative of the US population.
The Real Crisis of Parental Alienation and the Ongoing Defensive Tactics of Disinformation by Self-Promoting Deniers
Christine Giancarlo, Ph.D.
February 6, 2024
On January 11, 2024, Reem Alsalem, the appointed UN Special Rapporteur on Violence against Women and Girls, hosted a webinar titled: UN Special Report on Parental Alienation and Implications for Canada. She was joined by four panelists, all of whom represent various organizations seeking to eradicate violence against females. This is a worthy pursuit and a goal fully supported by all experts of parental alienation (PA). However:
– the topic of this webinar was parental alienation, not violence against women and girls.
– Alsalem has no research or other expertise in parental alienation that I am aware of
– there were no parental alienation experts on the panel.
– though violence is perpetrated almost equally by women as men in Canada and the United States, there were no violence against males group panelists.
Consistent with their familiar false narrative, Alsalem and her panelists reinforced the myth that parental alienation allegations are made by abusive fathers/partners seeking to escape prosecution. The peer-reviewed, scholarly and abundant parental alienation literature concludes otherwise.
Harman, Giancarlo, Lorandos & Ludmer’s (2023) Gender and Child Custody Outcomes Across 16 Years of Judicial Decisions Regarding Abuse and Parental Alienation, Children and Youth Services Review, https://doi.org/10.1016/j.childyouth.2023.10718
analyzed 500 Canadian trial level cases and found:
– only 10.9% of the 654 [abuse] allegations levied against alienated parents were founded or substantiated…this means that 90% of abuse allegations in cases of parental alienation were determined to be false or otherwise unsubstantiated.
– 25 “abusive” alienated parents were mothers and 10 were fathers, so the presumption that “abusive” alienated parents are mostly fathers is not reflected in these data… the base rate for these types of cases was very low (7.0%). Harman & Lorandos (2021) also found a low base rate of 7.9% using 967 U.S. appellate cases, so the two combined studies raise questions about how Meier et al. (2019), a prominent group of PA deniers, selected their cases, and how they obtained a large enough sample to statistically test their hypothesis.
– while Meier et al. (2019) reported that mothers’ allegations of abuse are discredited more often than fathers’ allegations of abuse, neither Harman and Lorandos (2021) nor this data-set could replicate or substantiate that claim
– replication studies have not found support for Meier et al.’s (2019) argument that their data indicate there is “widespread gender bias in courts’ handling of…abuse claims” (p. 26).
Books such as Gender and Domestic Violence: Contemporary Legal Practice and Intervention Reforms, edited by Russell & Hamel (2022), Oxford. https://doi.org/10.1093/medpsych/9780197564028.001.0001, clearly indicate that although there has been progress, arrest and intervention policies continue to reflect the gender paradigm, framing domestic violence in terms of male perpetrators and female victims. The book includes scholarly research spanning more than thirty years, highlighting the dire need for criminal justice reform.
And the book, Parental Alienation -Science and Law, edited by Lorandos & Bernet (2020), Carles C. Thomas Pub., https://www.amazon.com/Parental-Alienation-Science-DemosthenesLorandos/dp/0398093245 , details the scientific basis for testimony and legal decisions relating to parental alienation. Contributors to this book are mental health and legal experts.
These authors are not advocates; they are scientists. They do not engage in confirmation biases to cherry-pick their “conclusions” but instead rely exclusively on scientific methodology.
Alsalem was wrongfully appointed to report on a subject area in which she lacks professional and scientific competency. More recently, on January 23, 2024, Dr. Suzanne Zaccour, a self-described “feminist researcher and speaker”, sexual assault lawyer and Director of Legal Affairs at the National Association of Women and the Law, said in a press conference that the “victims of family violence (i.e., women) are being disbelieved, silenced, and punished based on the sexist
and unscientific theory of parental alienation”. https://www.ctvnews.ca/canada/ban-parental-alienation-arguments-in-family-law-cases-feministcoalition-urges-federal-government-1.6738592
Zaccour wrote a chapter in a book edited by two well-known parental alienation deniers, Mercer & Drew (2021), titled Parental alienation concepts and the law, an international perspective. This chapter, and in fact the book’s overarching theme, reflects not only the tired gender paradigm as false narrative but is a dangerous call for men and fathers to be assumed abusers and/or disposable. Zaccour and her colleagues openly dismiss the importance of fathers, especially as equal parents in their child’s life.
Dr. Jennifer Harman subsequently presented a response to Mercer & Drew’s book, Jennifer Harman -Ideology and Threats to Evidence-Based Decision-Making Regarding Families (2023) https://vimeo.com/865476523/635831dc65 and called out Zaccour’s dangerous incompetency in the subject area of parental alienation.
The following is a snapshot of Zaccour’s chapter (her words in italics):
p. 194:
– criticizes the shared parenting paradigm for its statement that each parent is equally important and responsible for the child.
– calls PA a “belief system supported by a minority of people and not supported by scientific evidence”.
-then says that “this belief system has benefited from the empirically dubious idea that fathers’ involvement should be increased and that children benefit from frequent and regular contact with both parents”.
-says fathers’ rights groups have adopted the “PA vocabulary” and “managed to convey in popular discourse the myth that fathers are the underdogs in custody litigation and that this is a grave injustice to be redressed”.
– and “people’s desire to make the world a simpler place is what leads to this reasoning (i.e., that a shared parenting paradigm is valid); science is not so definitive”.
-p. 195-196:
– “as the social science literature concludes” -there is no explanation of what literature Zaccouris referring to.
-says children do not do better in shared custody than in sole custody and some children, those in high-conflict situations fare worse, “in other words, courts are preventing children from seeing their mothers under the pretext that children need both parents”.
*Of note, both Zaccour and Joan Meier have not, to date, responded when asked by a real parental alienation expert (i.e., and psychiatrist) to provide the “social science literature” which they claim exists, nor provided Meier et al.’s (2019) “study” dataset and statistical model.
Other authors who contributed to Mercer & Drew’s (2021) edited book were Doughty and Drew, both lawyers and advocates for eradication of violence against women. They claim on p. 26 that parental alienation researchers use a strategy called “nefarious intent”, meaning these true PA scientists must have some wicked intent to cause harm. Doughty and Drew even claim that PA researchers appropriated Cold War language about brainwashing and mind-control to enliven Gardner’s (an early PA researcher) idea that some mothers induced false memories and beliefs in their children. *Of note, in reality, science has NO intent but to pursue truth. And children ARE highly suggestible, similar to cult indoctrination effects and Stockholm syndrome.
The editors Mercer and Drew (p. 14) claim that parental alienation researchers often assign sinister intentions to mothers, thus discrediting the mother before she can have the opportunity to fully present her case to the court.
And Meier’s chapter states that no one should question why a mother is using alienating behaviors to undermine a child’s relationship with its father. A mother must have a good reason for doing so as mothers only act in self defense and to protect their child.
*Of note, in a blatant double standard, Meier holds that a father can be assumed to be abusive if a mother has engaged in alienating him from their child.
Below are current rigorous reviews and a citation analysis of misinformation and disinformation about parental alienation:
Parental alienation is ubiquitous and deadly; its consequences are observed by teachers, professors, and mental health and legal experts on a daily basis. Children carry their trauma, guilt, and insecurities into adulthood, believing that their alienated parent did not love them. Or that the rejected parent was dangerous, incapable and/or unlovable. In my own experience over 32 years of university teaching, I have frequently heard from students still suffering from their undeserved and unnecessary fractured relationship with a parent. Children do not “grow out of it” (i.e., PA), nor do they usually reunite with that lost parent. Instead, they have lost 50% of themselves for much or all of their lives. Children DO need both parents; parents who are mentally healthy and safe. Parental alienators are child and former partner abusers; they engage in family violence and both fathers and mothers can be perpetrators, or victims.
On behalf of science, integrity and the urgent necessity to eradicate the massive social crisis of parental alienation, I urge the retraction of published fraudulent “research”, pseudo-scientific claims, media platforms that promote ignorance and damage, and false credentials by pretend experts.
UN Women Has 170 Twitter Accounts With Millions of Followers
Domestic Abuse and Violence International Alliance
February 6, 2024
UN Women is the largest and most powerful feminist organization in the world. UN Women currently has 170 Twitter accounts with millions of followers, mostly females. Many of the accounts are country-specific. Others focus on certain languages (eg, Arabic), regions (eg, the Pacific), cities (eg, Los Angeles), or groups (eg, youth).
Following is a listing of these accounts and the number of followers for each:
Tampon Dispenser Incident Highlights Growing Rejection of ‘Gender Agenda’
WASHINGTON / February 5, 2024 – School officials at Brookfield High School in Connecticut recently installed a tampon dispenser in the boys’ bathroom. Within minutes, male students at the school took action. Principal Marc Balanda dryly reported, “The installation was completed at 9:30 (a.m.). By 9:52, tampons were on the floor, the newly installed distribution box was ripped off the wall along with the masonry anchors, and the distribution box itself was destroyed.” (1)
A few days later on January 25, the Maine Judiciary Committee voted to kill the LD 1735, a bill that was designed to allow children from other states to travel to Maine, without parental consent, and become a ward of the state to receive cross-gender treatments (2).
The following day, the Utah legislature passed HB 257, which prohibits men who identify as women from accessing women’s bathrooms (3).
These events in Connecticut, Maine, and Utah reveal how the so-called “Gender Agenda,” which seeks to reshape society by defining the meaning of sex to include “gender identity,” is facing setbacks in both Democratic and Republican-led states.
Five judicial decisions, all handed down during the month of January, further underscore how the Gender Agenda is in retreat across the nation:
California: On January 10, federal judge Roger Benitez ordered the Escondido Union School District to reinstate two teachers who were placed on administrative leave for refusing to keep students’ gender transitions a secret from their parents (4).
Alabama: In 2022, the Alabama legislature passed the Vulnerable Child Compassion and Protection (VCAP) Act that banned the use of puberty blockers and cross-sex hormones for underage children. But the US Department of Justice challenged the law. On January 12, the 11th Circuit Court of Appeals lifted the injunction against the VCAP law, allowing its protections for children to go into effect (5).
Illinois: Disturbed by a long list of due process violations by the college, federal judge Colleen Lawless issued a restraining order on January 19 against the University of Illinois, allowing student Terrence Shannon to return to school (6).
Ohio: In late December, Gov. Mike DeWine vetoed House Bill 68, which sought to protect minors from transgender medical interventions and block males from competing against girls and women in sports. On January 24, the Ohio Senate voted to override the governor’s veto, allowing House Bill 68 to go into effect (7).
Florida: A federal judge dismissed a lawsuit on January 31 that had been filed by the Disney Corporation over the state legislature’s decision to alter the governing structure of Disney’s Reedy Creek Improvement District. Disney had argued in the lawsuit that the change to the district was made in retaliation for the organization’s criticism of the Parental Rights in Education Act (8).
In 2023, dozens of laws were enacted around the country to ban gender transitioning among underage students (9), uphold parental rights (10), stop pronoun mandates (11) and protect women’s sports (12).
A strong majority of Americans opposes the Gender Agenda (13). SAVE encourages lawmakers to work to block the Gender Agenda.
Why AI Image Recognition has the Power to Transform CPG Performance
An exponential increase in image data and rapid improvements in deep learning techniques make image recognition more valuable for businesses. For example, image recognition technology is used to enable autonomous driving from cameras integrated in cars. For an in-depth analysis of AI-powered medical imaging technology, feel free to read our research. As our exploration of image recognition’s transformative journey concludes, we recognize its profound impact and limitless potential.
The customizability of image recognition allows it to be used in conjunction with multiple software programs. For example, after an image recognition program is specialized to detect people in a video frame, it can be used for people counting, a popular computer vision application in retail stores. They can learn to recognize patterns of pixels that indicate a particular object.
Next, there is Microsoft Cognitive Services offering visual image recognition APIs, which include face and celebrity detection, emotion, etc. and then charge a specific amount for every 1,000 transactions. However, start-ups such as Clarifai provide numerous computer vision APIs including the ones for organizing the content, filter out user-generated, unsafe videos and images, and also make purchasing recommendations. In summary, image recognition technology has evolved from a novel concept to a vital component in numerous modern applications, demonstrating its versatility and significance in today’s technology-driven world. Its influence, already evident in industries like manufacturing, security, and automotive, is set to grow further, shaping the future of technological advancement and enhancing our interaction with the digital world.
Train your AI system with image datasets that are specially adapted to meet your requirements. In 2020, you, I, and everyone else took 1.12 trillion photos worldwide, according to a report from Rise Above Research, with a 25% increase projected for 2021. The following three steps form the background on which image recognition works. An image, for a computer, is just a bunch of pixels – either as a vector image or raster. In raster images, each pixel is arranged in a grid form, while in a vector image, they are arranged as polygons of different colors. For marketing teams and content creators, alternate text might not always be front-of-mind.
Personalization Techniques in Franchise Email Marketing
Finding the right balance between imperceptibility and robustness to image manipulations is difficult. Highly visible watermarks, often added as a layer with a name or logo across the top of an image, also present aesthetic challenges for creative or commercial purposes. Likewise, some previously developed imperceptible watermarks can be lost through simple editing techniques like resizing.
All-in-one Computer Vision Platform for businesses to build, deploy and scale real-world applications. Even the smallest network architecture discussed thus far still has millions of parameters and occupies dozens or hundreds of megabytes of space. SqueezeNet was designed to prioritize speed and size while, quite astoundingly, giving up little ground in accuracy.
It is also helping visually impaired people gain more access to information and entertainment by extracting online data using text-based processes. Image recognition helps self-driving and autonomous cars perform at their best. With the help of rear-facing cameras, sensors, and LiDAR, images generated are compared with the dataset using the image recognition software. It helps accurately detect other vehicles, traffic lights, lanes, pedestrians, and more.
Use AI-powered image classification to auto-tag images
The most significant difference between image recognition & data analysis is the level of analysis. In image recognition, the model is concerned only with detecting the object or patterns within the image. On the flip side, a computer vision model not only aims at detecting the object, but it also tries to understand the content of the image, and identify the spatial arrangement. Although both image recognition and computer vision function on the same basic principle of identifying objects, they differ in terms of their scope & objectives, level of data analysis, and techniques involved.
Subsequently, we will go deeper into which concrete business cases are now within reach with the current technology. And finally, we take a look at how image recognition use cases can be built within the Trendskout AI software platform. It involves many challenges, such as low-quality images, noise, occlusion, distortion, or variation. If you want to improve your image recognition, you need to overcome these challenges and optimize your results. The combination of these two technologies is often referred as “deep learning”, and it allows AIs to “understand” and match patterns, as well as identifying what they “see” in images. The key idea behind convolution is that the network can learn to identify a specific feature, such as an edge or texture, in an image by repeatedly applying a set of filters to the image.
Image recognition is a process of identifying and detecting an object or a feature in a digital image or video. It can be used to identify individuals, objects, locations, activities, and emotions. This can be done either through software that compares the image against a database of known objects or by using algorithms that recognize specific patterns in the image.
The final stage is classification, where the system assigns a label to the image based on the extracted features. This is done through various machine learning models or algorithms that compare the features with known categories ai image identification or labels to determine the presence of specific objects or features in the image. For instance, a dataset containing images labeled as ‘cat’ or ‘dog’ allows the algorithm to learn the visual differences between these animals.
SynthID isn’t foolproof against extreme image manipulations, but it does provide a promising technical approach for empowering people and organisations to work with AI-generated content responsibly.
Pictures or video that is overly grainy, blurry, or dark will be more difficult for the algorithm to process.
Data is transmitted between nodes (like neurons in the human brain) using complex, multi-layered neural connections.
Cloudinary, a leading cloud-based image and video management platform, offers a comprehensive set of tools and APIs for AI image recognition, making it an excellent choice for both beginners and experienced developers.
The initial layers typically recognize simple features like edges or basic shapes. As the data moves through the network, subsequent layers interpret more complex features, combining simpler patterns identified earlier into more comprehensive representations. This hierarchical processing allows the CNN to understand increasingly complex aspects of the image.
MIT News Massachusetts Institute of Technology
So it can learn and recognize that a given box contains 12 cherry-flavored Pepsis. Once an image recognition system has been trained, it can be fed new images and videos, which are then compared to the original training dataset in order to make predictions. This is what allows it to assign a particular classification to an image, or indicate whether a specific element is present. AI photo recognition and video recognition technologies are useful for identifying people, patterns, logos, objects, places, colors, and shapes.
Successful cosmetics, hair, and skincare brands know that data and metrics are essential when it comes to optimizing their team’s performance, improving compliance, and getting the most out of every.. In the future, this technology will likely become even more ubiquitous and integrated into our everyday lives as technology continues to improve. If a picture truly were worth a thousand words, those 7 trillion photos would be about 7 quadrillion words to search (who even talks in quadrillions?). With an average wordcount for adult fiction of between 70,000 and 120,000, that would mean over 73 billion books to go through. Explore the exciting Kentico Xperience feature AI Image Recognition for image alternative recognition, leveraging Microsoft Azure cognitive services.
These filters slid over input values (such as image pixels), performed calculations and then triggered events that were used as input by subsequent layers of the network. Neocognitron can thus be labelled as the first neural network to earn the label “deep” and is rightly seen as the ancestor of today’s convolutional networks. Everyone has heard about terms such as image recognition, image recognition and computer vision. However, the first attempts to build such systems date back to the middle of the last century when the foundations for the high-tech applications we know today were laid.
During data organization, each image is categorized, and physical features are extracted. Finally, the geometric encoding is transformed into labels that describe the images. This stage – gathering, organizing, labeling, and annotating images – is critical for the performance of the computer vision models. The images are inserted into an artificial neural network, which acts as a large filter.
It’s so fast and so seamless that you forget it’s on and doing its thing—and that’s the beauty of it. From now on, you can just get on with your work whilst artificial intelligence takes care of delivering valuable content and boosting your SEO results for you. Today’s vehicles are equipped with state-of-the-art image recognition technologies enabling them to perceive and analyze the surroundings (e.g. other vehicles, pedestrians, cyclists, or traffic signs) in real-time. Thanks to image recognition software, online shopping has never been as fast and simple as it is today. Levity is a tool that allows you to train AI models on images, documents, and text data. You can rebuild manual workflows and connect everything to your existing systems without writing a single line of code.If you liked this blog post, you’ll love Levity.
You need to improve your image recognition. Can AI-powered tools help you do it?
A deep learning model specifically trained on datasets of people’s faces is able to extract significant facial features and build facial maps at lightning speed. By matching these maps to the approved database, the solution is able to tell whether a person is a stranger or familiar to the system. Right off the bat, we need to make a distinction between perceiving and understanding the visual world. Various computer vision materials and products are introduced to us through associations with the human eye. It’s an easy connection to make, but it’s an incorrect representation of what computer vision and in particular image recognition are trying to achieve. The brain and its computational capabilities are the real drivers of human vision, and it’s the processing of visual stimuli in the brain that computer vision models are intended to replicate.
Test Yourself: Which Faces Were Made by A.I.? – The New York Times
We’ve previously spoken about using AI for Sentiment Analysis—we can take a similar approach to image classification. Image classifiers can recognize visual brand mentions by searching through photos. Computer Vision is a branch of AI that allows computers and systems to extract useful information from photos, videos, and other visual inputs.
The image recognition algorithm is fed as many labeled images as possible in an attempt to train the model to recognize the objects in the images. The automotive industry is witnessing a transformative shift with the advent of automated vehicle systems, where image recognition plays a pivotal role. Autonomous vehicles are equipped with an array of cameras and sensors, that continuously capture visual data. This data is processed through image recognition algorithms trained on vast, annotated datasets encompassing diverse road conditions, obstacles, and scenarios. These datasets ensure that the vehicle can safely navigate real-world conditions. The success of autonomous vehicles heavily relies on the accuracy and comprehensiveness of the annotated data used in their development.
Computers can use machine vision technologies in combination with a camera and artificial intelligence (AI) software to achieve image recognition. First, they can help you preprocess your images, such as resizing, cropping, filtering, or augmenting them, to improve their quality and diversity. Second, they can help you train and test your models, such as choosing the best algorithms, parameters, or metrics, to improve their performance and accuracy.
It’s used in various applications, such as facial recognition, object recognition, and bar code reading, and is becoming increasingly important as the world continues to embrace digital. The main aim of using Image Recognition is to classify images on the basis of pre-defined labels & categories after analyzing & interpreting the visual content to learn meaningful information. For example, when implemented correctly, the image recognition algorithm can identify & label the dog in the image. If you’re a legal service provider, legal team, or law firm interested in taking advantage of the power to be had from AI-based image recognition, contact Reveal to learn more.
One of the most important responsibilities in the security business is played by this new technology. Drones, surveillance cameras, biometric identification, and other security equipment have all been powered by AI. In day-to-day life, Google Lens is a great example of using AI for visual search. Companies can leverage Deep Learning-based Computer Vision technology to automate product quality inspection. While it takes a lot of data to train such a system, it can start producing results almost immediately. There isn’t much need for human interaction once the algorithms are in place and functioning.
In many administrative processes, there are still large efficiency gains to be made by automating the processing of orders, purchase orders, mails and forms. A number of AI techniques, including image recognition, can be combined for this purpose. Optical Character Recognition (OCR) is a technique that can be used to digitise texts. AI techniques such as named entity recognition are then used to detect entities in texts. But in combination with image recognition techniques, even more becomes possible. Think of the automatic scanning of containers, trucks and ships on the basis of external indications on these means of transport.
Image recognition can identify the content in the image and provide related keywords, descriptions, and can also search for similar images. Faster RCNN (Region-based Convolutional Neural Network) is the best performer in the R-CNN family of image recognition algorithms, including R-CNN and Fast R-CNN. Hardware and software with deep learning models have to be perfectly aligned in order to overcome costing problems of computer vision. Image Detection is the task of taking an image as input and finding various objects within it. An example is face detection, where algorithms aim to find face patterns in images (see the example below).
Image recognition models are trained to take an image as input and output one or more labels describing the image. Along with a predicted class, image recognition models may also output a confidence score related to how certain the model is that an image belongs to a class. To sum things up, image recognition is used for the specific task of identifying & detecting objects within an image. Computer vision takes image recognition a step further, and interprets visual data within the frame.
Depending on the number of frames and objects to be processed, this search can take from a few hours to days.
Another application for which the human eye is often called upon is surveillance through camera systems.
Image recognition can identify the content in the image and provide related keywords, descriptions, and can also search for similar images.
In raster images, each pixel is arranged in a grid form, while in a vector image, they are arranged as polygons of different colors.
The journey of image recognition, marked by continuous improvement and adaptation, mirrors the ever-evolving landscape of technology, where innovation is constant, and the potential for impact is limitless. Facial recognition technology is another transformative application, gaining traction in security and personal identification fields. These systems utilize complex algorithms trained on diverse, extensive datasets of human faces. These datasets are annotated to capture a myriad of features, expressions, and conditions. Some modern systems now boast accuracy rates exceeding 99%, a remarkable feat attributable to advanced algorithms and comprehensive datasets.
We help enterprises and public sector organizations transform unstructured images, video, text, and audio data into structured data, significantly faster and more accurately than humans would be able to do on their own. The platform comes with the broadest repository of pre-trained, out-of-the-box AI models built with millions of inputs and context. They detect explicit content, faces as well as predict attributes such as food, textures, colors and people within unstructured image, video and text data. While pre-trained models provide robust algorithms trained on millions of datapoints, there are many reasons why you might want to create a custom model for image recognition. For example, you may have a dataset of images that is very different from the standard datasets that current image recognition models are trained on. In this case, a custom model can be used to better learn the features of your data and improve performance.
Machine translation tools translate texts and speech in one natural language to another without human intervention. These were published in 4 review
platforms as well as vendor websites where the vendor had provided a testimonial from a client
whom we could connect to a real person. Evaluate 69 services based on
comprehensive, transparent and objective AIMultiple scores. For any of our scores, click the information icon to learn how it is
calculated based on objective data. Find out how the manufacturing sector is using AI to improve efficiency in its processes. Start by creating an Assets folder in your project directory and adding an image.
This process is expected to continue with the appearance of novel trends like facial analytics, image recognition for drones, intelligent signage, and smart cards. One of the biggest challenges in machine learning image recognition is enabling the machine to accurately classify images in unusual states, including tilted, partially obscured, and cropped images. This is a task humans naturally excel in, and AI is currently the best shot software engineers have at replicating this talent at scale. Opinion pieces about deep learning and image recognition technology and artificial intelligence are published in abundance these days. From explaining the newest app features to debating the ethical concerns of applying face recognition, these articles cover every facet imaginable and are often brimming with buzzwords. You can be excused for finding it hard to keep up with the hype, especially if your business doesn’t routinely intersect with high-tech solutions and you became interested in the capabilities of computer vision only recently.
This technology, once a subject of academic research, has now permeated various aspects of our daily lives and industries. Its evolution is marked by significant milestones, transforming how machines interpret and interact with the visual world. A compelling indicator of its impact is the rapid growth of the image recognition market. According to recent studies, it is projected to reach an astounding $81.88 billion by 2027. This remarkable expansion reflects technology’s increasing relevance and versatility in addressing complex challenges across different sectors. Our mission is to help businesses find and implement optimal technical solutions to their visual content challenges using the best deep learning and image recognition tools.
Top-1 accuracy refers to the fraction of images for which the model output class with the highest confidence score is equal to the true label of the image. Top-5 accuracy refers to the fraction of images for which the true label falls in the set of model outputs with the top 5 highest confidence scores. The big leap forward, into the realm of AI, happened in the 2000s, with the development of machine learning. This coincided with the new availability of massive datasets, thanks to the internet.
The working of a computer vision algorithm can be summed up in the following steps. Once the images have been labeled, they will be fed to the neural networks for training on the images. You can foun additiona information about ai customer service and artificial intelligence and NLP. Developers generally prefer to use Convolutional Neural Networks or CNN for image recognition because CNN models are capable of detecting features without any additional human input. Once the deep learning datasets are developed accurately, image recognition algorithms work to draw patterns from the images. For example, Google Cloud Vision offers a variety of image detection services, which include optical character and facial recognition, explicit content detection, etc. and charge per photo.
Currently, 46% of white Gen Z women — defined as women born between 1996 and 2010 –– identify as liberal, compared to only 28% of white Gen Z men. And a Pew Research study reveals that over half of white, liberal women have been diagnosed with a mental health condition at some point in their lives. This is twice the rate as young moderate or conservative women.
Does this mean there’s a correlation between progressive ideas and mental health?
As Gen Z women have become more progressive and politically active, Jonathan Haidt observes that they’ve shifted psychologically. Not only have they adopted a more external locus of control, but also have embraced an ideology that engenders cognitive distortions like catastrophizing and emotional reasoning. This has then caused them to become more anxious and depressed.
Young liberal women are also much less tolerant. Democrat women are three times more likely to block friends on social media because of their political views than are Republican women: 30% versus 10%.
The liberal narrative portrays marriage and families as threats to personal freedom. It casts any form of commitment or responsibility as a constraint. Accordingly in the United States, 45% of women are predicted to be childless and unmarried by 2030. In the UK, more than half of women aged 34 or under are now unmarried.
Surprisingly, liberal women are much more accepting of marital infidelity. Liberal women are halfas likely as conservative women to believe it’s always wrong for a married woman to have an affair: 36% versus 71%.
Very liberal women are nearly three times more likely to reporting having experienced sexual harassment than conservative women: 71.7% among very liberal women versus 27% among conservative women.
Young liberal women are much less likely to date people with different political views than are conservative women. While more than half of men said they would date someone with different views, just 35% of women said the same thing.
Former University of Ottawa professor Janice Fiamengo sums up the pathological effects of feminist ideology this way:
“Feminism was never sane. It was never without deep rancor and bitterness against men, never free from the claim that women were absolute victims of male predation, never uninterested in destroying the family, never accurate in its claims about women’s social situation, never unwilling to slander men in the most vicious and unpitying ways, and it never expressed any appreciation for men nor recognition that men had made any contribution to society or that men had ever acted out of love and concern and compassion for women in the laws that had been made or social instruments that had been developed over time. It was always a deeply misandrist, man-hating, man-blaming kind of movement.”
Popular Support for Campus ‘Kangaroo Courts’ is Collapsing
WASHINGTON / January 24, 2024 – Campus Kangaroo Courts have reached the point that even the kangaroos are becoming embarrassed. Case in point is a recent judicial decision involving the University of Illinois.
Last week, Judge Colleen Lawless granted a restraining order against the university, allowing Terrence Shannon to return to his classes and varsity sports activities. In her decision, Judge Lawless enumerated a lengthy list of due process violations (1):
Shannon had not been informed of the accuser’s name or given access to the evidence used against him.
The university did not investigate the allegation or “weigh the credibility of the evidence in light of the nature of the allegation.”
Shannon had not been allowed to attend the hearing.
The university issued its ruling “without any findings of fact or reasoning for the decision.”
When Shannon rejoined his team on the court, the crowd greeted him with whistles, towel-waving, and sustained applause (2).
In years past, a student accused of sexual assault likely would have faced fevered protests and petitions demanding his immediate removal (3). But the tide of public opinion is turning.
One lawsuit recently filed against George Mason University opened with this laughable introduction (4):
“George Mason University would rather lose in court than lose in the press. In its handling of false misconduct allegations against Mr. Wright, the University repeatedly and flagrantly violated Title IX regulations and its own policies. In a clear showing of bias, the University hosted Mr. Wright’s false accuser as a #metoo speaker on campus, paid her and her co-conspirator hundreds of thousands of dollars each, made public statements in support of her and against Mr. Wright, retaliated against him for his lawsuit, and used different standards.”
The Title IX high-jinks are taking a financial toll, as well.
In August, a jury awarded $4 million to Peter Steele whose sexual assault case was mishandled by Pacific University, ruling the institution had intentionally caused the man emotional distress (5).
Then in December, a Philadelphia jury awarded Dr. John Abraham a record-setting $15 million award for egregious Title IX offenses by Thomas Jefferson University (6).
Even state Supreme Courts are losing patience with Title IX over-reach. In June, the Connecticut Supreme Court ruled that Yale University’s Title IX procedures “lacked important procedural safeguards,” opening the door to costly defamation lawsuits against the institution (7).
Then in January, the Washington Supreme Court weighed in, ruling that Washington State University was not liable for protecting a student from a sexual assault that occurred off-campus (8).
Attorney Scott Greenfield has posited that “activists sought to increase their powers on campus to control the actions of their male peers, while ignoring whether it had anything to do with the purposes of Title IX” (9). Indeed, there is a growing perception that campus Title IX offices are staffed by gender ideologues, not legal professionals (10).
