As we come to realize after a long and trying time in 2020 with the effect of the pandemic 2020. It became an understanding that the use of technology became more fundamentally important to us with lockdown being implemented. Hence with the development of artificial intelligence.
Artificial intelligence (AI) is evolving — literally. Researchers have created software that borrows concepts from Darwinian evolution, including “survival of the fittest,” to build AI programs that improve generation after generation without human input. The program replicated decades of AI research in a matter of days, and its designers think that one day, it could discover new approaches to AI.
While most people were taking baby steps, they took a giant leap into the unknown,” says Risto Miikkulainen, a computer scientist at the University of Texas, Austin, who was not involved with the work. “This is one of those papers that could launch a lot of future research.”
Building an AI algorithm takes time. Take neural networks, a common type of machine learning used for translating languages, and driving cars. These networks loosely mimic the structure of the brain and learn from training data by altering the strength of connections between artificial neurons. Smaller subcircuits of neurons carry out specific tasks — for instance, spotting road signs — and researchers can spend months working out how to connect them so they work together seamlessly.
In recent years, scientists have sped up the process by automating some steps. But these programs still rely on stitching together ready-made circuits designed by humans. That means the output is still limited by engineers’ imaginations and their existing biases.
So Quoc Le, a computer scientist at Google, and colleagues developed a program called AutoML-Zero that could develop AI programs with effectively zero human input, using only basic mathematical concepts a high school student would know. “Our ultimate goal is to actually develop novel machine learning concepts that even researchers could not find,” he says.
The program discovers algorithms using a loose approximation of evolution. It starts by creating a population of 100 candidate algorithms by randomly combining mathematical operations. It then tests them on a simple task, such as an image recognition problem where it has to decide whether a picture shows a cat or a truck.
In each cycle, the program compares the algorithms’ performance against hand-designed algorithms. Copies of the top performers are “mutated” by randomly replacing, editing, or deleting some of its code to create slight variations of the best algorithms. These “children” get added to the population, while older programs get culled. The cycle repeats.
The system creates thousands of these populations at once, which lets it churn through tens of thousands of algorithms a second until it finds a good solution. The program also uses tricks to speed up the search, like occasionally exchanging algorithms between populations to prevent any evolutionary dead ends, and automatically weeding out duplicate algorithms.
Artificial Intelligence on Cyber Security
There is currently a big debate raging about whether Artificial Intelligence (AI) is a good or bad thing in terms of its impact on human life. With more and more enterprises using AI for their needs, it’s time to analyze the possible impacts of the implementation of AI in the cybersecurity field.
The positive uses of AI for cybersecurity
Biometric logins are increasingly being used to create secure logins by either scanning fingerprints, retinas, or palm prints. This can be used alone or in conjunction with a password and is already being used in most new smartphones. Large companies have been the victims of security breaches that compromised email addresses, personal information, and passwords.
Cybersecurity experts have reiterated on multiple occasions that passwords are extremely vulnerable to cyber attacks, compromising personal information, credit card information, and social security numbers. These are all reasons why biometric logins are a positive AI contribution to cybersecurity.
AI can also be used to detect threats and other potentially malicious activities. Conventional systems simply cannot keep up with the sheer number of malware that is created every month, so this is a potential area for AI to step in and address this problem. Cybersecurity companies are teaching AI systems to detect viruses and malware by using complex algorithms so AI can then run pattern recognition in software. AI systems can be trained to identify even the smallest behaviors of ransomware and malware attacks before it enters the system and then isolate them from that system. They can also use predictive functions that surpass the speed of traditional approaches.
Systems that run on AI unlock potential for natural language processing which collects information automatically by combing through articles, news, and studies on cyber threats. This information can give insight into anomalies, cyber attacks, and prevention strategies. This allows cybersecurity firms to stay updated on the latest risks and time frames and build responsive strategies to keep organizations protected.
AI systems can also be used in situations of multi-factor authentication to provide access to their users. Different users of a company have different levels of authentication privileges which also depend on the location from which they’re accessing the data. When AI is used, the authentication framework can be a lot more dynamic and real-time and it can modify access privileges based on the network and location of the user. Multi-factor authentication collects user information to understand the behavior of this person and decide about the user’s access privileges.
To use AI to its fullest capabilities, it must be implemented by the right cybersecurity firms that are familiar with its functioning. Whereas in the past, malware attacks could occur without leaving any indication on which weakness it exploited, AI can step in to protect the cybersecurity firms and their clients from attacks even when multiple skilled attacks are occurring.
Drawbacks and limitations of using AI for cybersecurity
The benefits outlined above are just a fraction of the potential of AI in helping cybersecurity, but some limitations are preventing AI from becoming a mainstream tool used in the field. To build and maintain an AI system, companies would require an immense amount of resources including memory, data, and computing power.
Additionally, because AI systems are trained through learning data sets, cybersecurity firms need to get their hands on many different data sets of malware codes, non-malicious codes, and anomalies. Obtaining all of these accurate data sets can take a really long time and resources which some companies cannot afford.
Another drawback is that hackers can also use AI themselves to test their malware and improve and enhance it to potentially become AI-proof. In fact, AI-proof malware can be extremely destructive as they can learn from existing AI tools and develop more advanced attacks to be able to penetrate traditional cybersecurity programs or even AI-boosted systems.
Solutions to AI limitations
Knowing these limitations and drawbacks, it’s obvious that AI is a long way from becoming the only cybersecurity solution. The best approach in the meantime would be to combine traditional techniques with AI tools, so organizations should keep these solutions in mind when developing their cybersecurity strategy:
- Employ a cybersecurity firm with professionals who have experience and skills in many different facets of cybersecurity.
- Have your cybersecurity team test your systems and networks for any potential gaps and fix them immediately.
- Use filters for URLs to block malicious links that potentially have a virus or malware.
- Install firewalls and other malware scanners to protect your systems and have these constantly updated to match redesigned malware.
As the potential of AI is being explored to boost the cybersecurity profile of a corporation, it is also being developed by hackers. Since it is still being developed and its potential is far from reach, we cannot yet know whether it will one day be helpful or detrimental for cybersecurity. In the meantime, organizations must do as much as they can with a mix of traditional methods and AI to stay on top of their cybersecurity strategy.
Artificial Intelligence in COVID- 19 Pandemic
It can be understood during the COVID-19 pandemic, health care professionals and researchers have been confined mostly to using local and national datasets to study the impact of comorbidities, pre-existing medication use, demographics, and various interventions on disease course.
Multiple organizations are running an initiative to accelerate global collaborative research on COVID-19 through access to high-quality, real-time multi-center patient datasets. The National Science Foundation has provided funding to develop the Records Evaluation for COVID-19 Emergency Research (RECovER) initiative.
They are using the technology to find trends and data connections to help better understand and treat COVID-19, with a special emphasis on the impact existing medications have on COVID-19.
This approach allows a health care professional or researcher to identify patterns in patient responses to drugs, select or rank the predictions from our platform for drug repurposing, and evaluate their responses over time. This will help with COVID-19 and other potential pandemics.
Artificial Intelligence can inform public health decision-making amid the pandemic.
A new model for predicting COVID-19’s impact using artificial intelligence (AI) dramatically outperforms other models, so much so that it has attracted the interest of public health officials across the country.
While existing models to predict the spread of a disease already exists, few, if any, incorporate AI, which allows a model to make predictions based on observations of what is actually happening — for example, increasing cases among specific populations — as opposed to what the model’s designers think will happen. With the use of AI, it is possible to discover patterns hidden in data that humans alone might not recognize.
AI is a powerful tool, so it only makes sense to apply it to one of the most urgent problems the world faces,” says Yaser Abu-Mostafa (Ph.D. ’83), professor of electrical engineering and computer science, who led the development of the new CS156 model (so-named for the Caltech computer science class where it got its start).
The researchers evaluate the accuracy of the model by comparing it to the predictions of an ensemble model built by the Centers for Disease Control and Prevention from 45 major models from universities and institutes across the country. Using 1,500 predictions as points of comparison with the CDC ensemble, the researchers found that the CS156 model was more accurate than the ensemble model 58 percent of the time as of November 25.
Abu-Mostafa is currently expanding the CS156 model based on feedback from public health officials in the hope that it can be a lifesaving tool to guide policy decisions.
This model is being modified to allow public health officials to predict how various interventions — like mask mandates and safer-at-home orders — affect control of the spread of the disease. Armed with those predictions, public health officials would be better able to evaluate which interventions are most likely to help.
At the end of it all, it is an undeniable fact that AI is at the center of a new enterprise to build computational models of intelligence. The main assumption is that intelligence (human or otherwise) can be represented in terms of symbol structures and symbolic operations which can be programmed in a digital computer.
There is much debate as to whether such an appropriately programmed computer would be a mind, or would merely simulate one, but AI researchers need not wait for the conclusion to that debate, nor for the hypothetical computer that could model all of the human intelligence, however, we cannot deny the fact of the contribution of AI on cybersecurity and public health.
Previously published on medium
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