Mind Blowing AI Trends, Applications for 2022 and Beyond
Trends, Applications & What will AI be like in 2030? Lets find out
There once was — and still is — a clear misconception among the masses that Artificial Intelligence will take over the human civilization and robots will become our overlords! Thus, ensuring the end of humanity on the blue planet.
However, much of this ‘hysteria’ was before the adoption of AI across diverse industries. Now, tech leaders are expecting a new age in AI that will be way more impactful to our lives than many of the other scientific wonders of the last century.
Dominant Role in Cybersecurity
Cybercriminals are declared to be the enemy number one of society by many industry watchdogs.
In 2020, the US alone faced over 1001 cyberattacks on businesses and individuals, exposing over 155.8 million records.
With the dominance of automation and wide usage of machines in processes, a small security loophole in even the auxiliaries makes the whole system vulnerable to cyberattacks.
It is relatively easier to pinpoint the point of failure in smaller systems. But in more complex structures, it becomes harder to even find the origin of the attack.
One of the things AI is great at is at picking patterns and pointing out anomalies should they occur. This could be an impregnable line of defence against hackers to stop them from getting into the system.
The net for the use cases of AI in cybersecurity is widening, with invention of new AI web based malware scanners like WP hacked help , and we are hoping to see immense progress in this field.
Combining computer vision’s neural network approaches with text analytics encompassing taxonomies and machine learning can protect organizations & websites(especially wordpress) from malware attacks like japanese keyword hack to preclude the success of costly phishing attacks.
Development of AI Through AI
One of the hurdles in the wide-scale adoption of AI across industries is the lack of acumen and expertise of AI engineers in developing systems and processes for tools and algorithms.
The crisis is even reminiscent of earlier days of web development when it took experienced developer days to set up a simple website. However, the problem in the development of AI is taken care of by itself.
No-code and low-code techniques are developed with the aim to produce complex, multi-dimensional AI systems using existing AI tools.
This has been possible by developing simple interfaces and modular designs that can readily accept and process domain-specific data.
Combined with natural language processing and modelling, it is possible that soon the bulk of development in Artificial Intelligence is carried out through simple texts or verbal commands.
The pace of development in AI is unprecedented and it will continue to be so in the coming years.
Mind-blowing applications of AI
Despite the technology’s relative novelty, the general public as well as businesses have already witnessed numerous applications of AI and are, by now, convinced about AI’s ability to replicate human thought and assist in performing cognitive and creative tasks.
Artificial intelligence has quickly grown from being a distant hope to a casual part of the present reality. Computer programs capable of performing human-like cognitive and computational tasks without human intervention are rapidly growing in capability as well as ubiquity. Every new AI application that emerges expands the limits of what the technology can achieve, leaving us in awe and excitement for what the future holds. Following are a few mind-blowing applications of AI that will definitely make you reconsider the limits of what’s possible:
1. Crime Prevention
Machine learning-enabled AI applications are being developed and used by law enforcement bodies in various countries to predict and prevent crimes. Japan is considering the use of AI and big data for predicting crime, which will enable the law enforcement authorities there to prevent criminal activities by proactively dispatching patrols to high-risk areas. Application of AI in predicting crime incidences is not a new concept and is already in different stages of development in countries like the US, UK, and China.
2. Personal Assistance
The most commonplace, and hence the most underrated application of AI are the Personal AI assistants. Personal AI assistants like Siri and Cortana can not only enable you to operate your phones using your voice but can interact with you like a human and can even engage in banter in some cases. The assistant programs use machine learning to continuously gain information on users through interaction and provide them with highly customized results and responses.
Yes, you read that right - AI can be programmed to read people’s minds. Scientists have researched and developed AI programs that can scan your brain’s blood flow to trace mental activities and decipher the thoughts associated with the detected brain activity. The AI system can detect the picture produced in the subject’s mind while looking at a real image. While the system isn’t perfect yet, training it with more data and images will enable the AI mind-reader to decipher mental images with greater depth and accuracy.
4. Fashion Design
In addition to performing cognitive tasks, AI can also be trained to perform creative tasks. A new application of AI, that has opened up new avenues for the future, is in the field of fashion. Amazon has developed an AI fashion designer that can be trained to design clothes in any desired style. This AI fashion designer can be trained using images that represent a single style. Using these images as reference, the AI generates new designs that are consistent in style.
5. Heart Diagnosis
Artificial intelligence can not only read people’s minds but also their hearts! New AI applications powered by machine learning algorithms and trained using historical data can predict heart attacks better than doctors. Google’s new AI algorithm can predict the risk of heart attacks by simply scanning a patient’s eyes, with a considerably high success rate. More sophisticated versions of these algorithms can save hundreds, if not thousands, of lives by predicting heart attacks way before they occur.
6. AI Creation
The most mind-blowing, albeit a little unsettling, application of AI is building other AI programs. Google researchers experimented with this concept when they instructed an AI application to create another AI application that would recognize objects in a video. The resulting “child” AI that was created outperformed the man-made AI in the given task. This shows AI’s capacity to perform complex tasks and to learn and evolve without supervision, much like us humans.
Until recently, the primary method for mold testing and mold speciation was viable laboratory testing. This means certified mycologists examining a cultured specimen under a microscope. However, with huge leaps in artificial intelligence (AI) and machine learning, it is now possible to skip the lab and go straight for mold removal , thereby reducing wait times drastically and eliminating human error. Simply put, machine learning system for mold identification works like any image recognition software: it scans an image (in this case, an image of a mold spore) and browses its database of mold images for a match based on various criteria like colour, cell structure and hyphae. Once it finds a match, it is able to identify the exact species of mold you are dealing with.
What will AI be like in 2030?
By 2030 , AI will likely no longer be getting adopted with simple scenarios and applications. Today, AI is evolving across all three dimensions — compute, data and algorithm — which sets the context for its adoption across all realms of life and work by 2030. Here is the direction that I see AI moving within these categories.
Out of all the principal factors driving the evolution of AI, compute is the easiest to quantify. In the coming decade, computing is going to witness a major transformation. Graphical processing units (GPU) are making way for Application-Specific Integrated Circuits (ASIC) and Field Programmable Gate Array (FPGA). This is because both ASIC and FPGA showcase better performance than GPU.