A Quick Guide to Analyzing Apache Logs on Alibaba Cloud Log Service

| March 23, 2018

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With Alibaba Cloud Log Service, there are several methods available for you to collect upstream data. You can use the built-in LogSearch and LogAnalytics functions, or you can deploy the more familiar ElasticSearch, Logstash, and Kibana (ELK) stack. In this article, we will discuss how you can build your own ELK stack on Alibaba Cloud Log Service to analyze and monitor Apache logs.

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AI TO READ HUMAN THOUGHTS AND COVERT THEM INTO TEXTS

Article | April 2, 2020

Imagine an age where you can read the thoughts of a person via telepathy like Professor X of X-Men comics and uncover their anarchical plans or a tech that reads the thoughts of a mute person or your pets and helps you have better communication. Well, a team at the University of California, San Francisco, performed this experiment and put us a step closer to the dream. Joseph Makin, co-author of the research team says, “We are not there yet, but we think this could be the basis of a speech prosthesis.” The university developed the AI to decipher up to 250 words in real-time from a set of between 30 and 50 sentences. The university recruited four women participants with a history of epilepsy and already had electrode arrays implanted in their brain to monitor epileptic seizures. These participants were asked to read aloud from 50 set sentences multiple times as the team tracked their neural using electrodes while they were speaking. The sample included “Tina Turner is a pop singer”, “the oasis was a mirage”, “part of the cake was eaten by the dog”, “Those thieves stole 30 jewels”, “how did the man get stuck in the tree” and “the ladder was used to rescue the cat and the man.” The largest group of sentences contained 250 unique words.

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Artificial Intelligence 2020 Stories: The Great, the Glowing and the Gross Truths

Article | January 4, 2021

2020 has been an unprecedented year where we have seen more downs than ups. COVID-19 has impacted every aspect of our lives. But when it comes to digitisation and Artificial Intelligence, we have seen some impactful developments and achievements. As we approach the end of 2020, it is worth to look back at these AI stories to highlight the truths and discuss what it means for AI future direction. The Great Truth: Artificial intelligence played a crucial role in the detection and fight against COVID-19. Indeed, we have seen the emergence of the use of AI at hospitals to evaluate chest CT scans. With the use of deep learning and image recognition, COVID patients were diagnosed thus enabling the medical team to follow the necessary protocols. Another application was the triage of COVID-19. Once a patient has been diagnosed with COVID, AI has been used to predict the likely severity of the illness so the medical staff can prioritize resources and treatments. COVID has highlighted the need to deploy intelligent autonomous agents. As a result, we have seen both robots used at hospitals to diagnose COVID-19 patients and drones deployed to monitor if the public is adhering to social distancing rules. Another major AI contribution in the fight against COVID-19 is in the area of vaccine and drug discovery. Moderna’s vaccine that has been approved by US Food and Drugs Administration has used machine learning to optimise mRNA sequencing. The above is a proof that AI can make great contribution to mankind if it is used for “good”. The Glowing Truths: Some impressive AI results have been achieved. However, to leap forward a holistic and sustainable approach is needed. 2020 has seen some great AI achievements and leaps forward. The first example is Deepmind’s AlphaFold. The model scored highest at the Critical Assessment of Structure Prediction competition. The algorithm takes genetic information as inputs and outputs a three-dimensional structure. The model has impressively addressed a 50-year-old challenge of figuring out want shapes proteins fold into known as the “protein folding problem”. While Deepmind’s AlphaFold is a great achievement, it is noted by some scientists that it is unclear how the model will work with more real-world complex proteins. Thus, more work is needed in this area. The second example is OpenAI’s GPT3. The model is a very large network composed of 96 layers and 175 billion parameters. The model has shown impressive results for several tasks such as NLP questions & answering and generating code. However, it is noted that the model does not have any kind of reasoning and does not understand what it is generating. Furthermore, its large size makes it very expensive. It is also unsustainable carbon footprint wise; its training is equivalent to driving a car to the moon and back. While both AlphaFold and GPT3 models are both impressive achievements, there are some philosophical challenges/ questions that need to be addressed/ answered. The first question is about games/ simulated worlds vs. real world examples. Most often algorithms/models succeed in simulated world but fail in real world as the environment is more complex. How can we close the gap? How can we make the AI models succeed with complex tasks? I guess the first step is to apply AI to a real-world example with varied complexity levels. The second question is about the structure and the size of AI models. Do models have to be big? Can we come up with a new generation of algorithms/ models that are smaller is size and have more efficient computations? Well to answer this question we have to take a pause on deeplearning and explore new venues. The Gross Truths: Ethics and bias remain the main drawbacks of Artificial Intelligence. Over the last year, we had several prominent examples of AI ethics and bias issues. The first example relates to facial recognition: after several calls against mass surveillance, racial profiling and bias, and in light of Black Lives Matter movement starting in the United States, several tech companies such as Microsoft banned the police from using its facial recognition technology. The second example relates to the use of an algorithm to predict exam results during COVID-19 period: after accusations and protests that the controversial algorithm was biased against students from poorer backgrounds, the United Kingdom government was forced to ditch the algorithm. In the absence of regulations and tightened frameworks, ethics and bias will continue to be the main concerns surrounding the use of artificial intelligence. Looking into the future, AI adoption will continue to accelerate, and we will probably see more breakthroughs achieved by only if we start looking at the subject in a holistic and sustainable view. Focusing models on real world problems and reducing the models carbon footprint will be a major step forward. We need to move away from thinking that “more” is always “more”. Sometimes “more” is “less”.

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Introducing new JVM language Concurnas

Article | March 5, 2020

Concurnas is a new general purpose open source JVM programming language designed for building concurrent, distributed and parallel systems. Concurnas is easy to learn; it offers incredible performance as well as many features for building modern, enterprise scale computer software. What distinguishes Concurnas from existing programming languages is that it presents a unique, simplified means of performing concurrent, distributed and parallel computation. These forms of computation are some of the most challenging in modern software engineering, but with Concurnas they are made easy.

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IN AI (CAN) WE TRUST?

Article | April 20, 2021

Artificial intelligence (AI) is the best thing to happen to our lives. It helps us read our emails, complete our sentences, get directions, do online shopping, get dining and entertainment recommendations, and even make it easier to connect with old friends or make new ones on social media. AI is not only skilling itself at many human jobs; it is also making decisions for us. The question is whether these decisions can be trusted. To elaborate, does AI-aided recruitment facilitate or reject the right candidate selection? Is the Tinder match made in heaven or by the algorithm? Who is being sent to jail — criminals or innocents predicted by AI bias? As humans, we come from a diverse range of sociopolitical, racial and cultural backgrounds. The idea of what is right — and the mere question of morality itself — changes depending on the context. How does the AI decide what is right — and for whom? Faced with the decision to save the driver in a smart car or the pedestrian, who does the onboard AI choose? How does it arrive at this decision? "Debiasing humans is harder than debiasing AI systems," believes Olga Russakovsky, an assistant professor in the Department of Computer Science at Princeton University A Question Of Ethics Before AI can think for humans, humans have to think for AI. Essentially, the ethics of AI technology is the embodiment of its creators' ethics. And this is where the "ethical AI conundrum" begins. AI is good and evil, but the truth is that the underlying concern that dominates every invention or innovation is human bias. There is enough evidence pointing in this direction, the recent and most prominent one being Apple. In 2019, the company's new credit card was accused of offering some women a lower limit despite them having better credit scores than their male spouses. Of such intensity was the bias that Apple co-founder Steve Wozniak noted that his wife got a lower credit limit than he did despite the fact that they had "no separate bank or credit card accounts or any separate assets." AI is open to biases because it makes decisions based on its human creators' information, and this information contains biases. Many of the creators are males who grew up in the western world, which can predispose them to individual communities and geographies. There has been enough debate around COMPAS (Correctional Offender Management Profiling for Alternative Sanctions), an algorithm that courts in the United States devised to anticipate the likelihood of repeat offenders. The algorithm indicated twice as many false positives for black offenders (45%) as white offenders (23%). Garbage In, Garbage Out TechTarget defines the concept of "garbage in, garbage out" this way: "The quality of the input determines the quality of output." Apart from humans, bias can also permeate a machine's intelligence. After all, as B Nalini noted, it is humans who frame the problem, train the model and deploy the system. Even with unbiased data, there is no guarantee of accuracy, as the very process by which machine learning models achieve this can yield biased outcomes. Teaching AI Morality In a 2001 article, futurist and inventor Raymond Kurzweil stated that our view of progress is linear. The more we adapt to change, the rate of change itself increases exponentially. We may expect to see 20,000 years of progress in the decades encompassing the 21st century. However, even while we acknowledge the exponential growth, we must also accept that AI is a relatively new technology. The word itself came into existence a mere 60 years ago, meaning we are closer to the beginning or maybe even in the middle rather than the end. AI is just a toddler, learning the differences between moral right and wrong and inheriting its creators' biases. It still struggles to do much more than detect statistical patterns in large datasets. Human understanding and intelligence extend far beyond static ideas of right and wrong, the rules themselves changing according to sociocultural and historical contexts. If, as humans, we are still struggling with morality, it is rather presumptuous of us to expect a machine — that we have created — to outshine us in this regard. As the Harvard Business Review noted, there are two conclusions. The first involves acknowledging how AI can help improve the process of human decision-making itself by predicting outcomes from available data while disregarding variables that lead human decision-makers to generalize and segregate without even realizing their inherent biases. The second alludes to a more complicated need to technically define and measure the ever-fleeting idea of "fairness." Conclusion Bias is as fundamental as the air we breathe or the environment we live in, and it is prevalent among us all, either as individuals or as a community. At this point in human history, the world is getting ready to industrialize AI tech and deploy it more widely. Thus, addressing the "inherent" AI biases at this moment becomes exceptionally critical. AI is just a toddler, learning the differences between moral right and wrong and inheriting its creators' biases. If, as humans, we are still struggling with morality, it is rather presumptuous of us to expect a machine that we have created will outshine us in this regard Just as a pet blindly mirrors its trainer's instructions and personality, AI mirrors its creators' input, biased or not. Thus, the root of the problem goes far deeper than AI ethics but becomes a question of human morality and the concept of "fairness" itself and how it can be defined and measured. "Debiasing humans is harder than debiasing AI systems," believes Olga Russakovsky, an assistant professor in the Department of Computer Science at Princeton University and co-founder of the AI4ALL Foundation, which works to increase diversity and inclusion within AI. "I am optimistic that automated decision making will become fairer," she mentioned in an interview with Wired. First printed in Forbes on Feb 9, 2021Enable GingerCannot connect to Ginger Check your internet connection or reload the browserDisable in this text fieldRephraseRephrase current sentenceEdit in Ginger×

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Unisys

Unisys is a global information technology company that works with many of the world's largest companies and government organizations to solve their most pressing IT and business challenges. Unisys specializes in providing integrated, leading-edge solutions to clients in the government, financial services and commercial markets.

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