At TRI, our goal is to make breakthrough capabilities in Artificial Intelligence (AI). Despite recent advancements in AI, the large amount of data collection needed to deploy systems in unstructured environments continues to be a burden. Data collection in computer vision can be both quite costly and time-consuming, largely due to the process of annotating. Annotating data is typically done by a team of labelers, who are provided a long list of rules for how to handle different scenarios and what data to collect. For complex systems like a home robot or a self-driving car, these rules must be constantly refined, which creates an expensive feedback loop.
There is no denying that Artificial Intelligence (AI) is the future of cybersecurity. In other words, the future of cybersecurity lies in the hands of Artificial Intelligence (AI). Companies or medium-sized corporations can counter various cyber threats using the advanced concepts of AI. If you want to know about different AI predictions that will positively influence cybersecurity in 2021 and in the future, read this post in detail. According to a recent research conducted by Trend Micro, Artificial Intelligence (AI) will replace the need for human beings by the end of 2030.
Most artificial intelligence is still built on a foundation of human toil. Peer inside an AI algorithm and you'll find something constructed using data that was curated and labeled by an army of human workers. Now, Facebook has shown how some AI algorithms can learn to do useful work with far less human help. The company built an algorithm that learned to recognize objects in images with little help from labels. The Facebook algorithm, called Seer (for SElf-supERvised), fed on more than a billion images scraped from Instagram, deciding for itself which objects look alike. Images with whiskers, fur, and pointy ears, for example, were collected into one pile.
The technology sector has been hit hard as of late, as the impending economic reopening has gotten more attention, and rising long-term bond rates have hit growth stocks particularly hard. As rates go up, future earnings are discounted more, harming valuations for growth stocks and increasing attention on value stocks that make profits today. And yet, technology will still play an ever-increasing role in society even post-pandemic. AI helps businesses make sense of their vast troves of data, glean insights, and react quickly in an automated fashion. As AI helps grow revenue and cut costs at the same time, it will be a mission-critical capability for any large company, even post-pandemic. But are there really any AI stocks that still trade at reasonable valuations, and which can handle the market's current value rotation?
Harm wrought by AI tends to fall most heavily on marginalized communities. In the United States, algorithmic harm may lead to the false arrest of Black men, disproportionately reject female job candidates, or target people who identify as queer. In India, those impacts can further impact marginalized populations like Muslim minority groups or people oppressed by the caste system. And algorithmic fairness frameworks developed in the West may not transfer directly to people in India or other countries in the Global South, where algorithmic fairness requires understanding of local social structures and power dynamics and a legacy of colonialism. That's the argument behind "De-centering Algorithmic Power: Towards Algorithmic Fairness in India," a paper accepted for publication at the Fairness, Accountability, and Transparency (FAccT) conference, which begins this week. Other works that seek to move beyond a Western-centric focus include Shinto or Buddhism-based frameworks for AI design and an approach to AI governance based on the African philosophy of Ubuntu.
Securing vast and growing IoT environments may not seem to be a humanly possible task--and when the network hosts tens or hundreds of thousands of devices the task, indeed, may be unachievable. To solve this problem, vendors of security products have turned to a decidedly nonhuman alternative: artificial intelligence. "Cyberanalysts are finding it increasingly difficult to effectively monitor current levels of data volume, velocity and variety across firewalls," CapGemini noted in a survey research report, "Reinventing Cybersecurity With Artificial Intelligence." The report also noted that traditional methods may no longer be effective: "Signature-based cybersecurity solutions are unlikely to deliver the requisite performance to detect new attack vectors." In addition to conventional security software's limitations in IoT environments, CapGemini's report revealed a weakness in the human element of cybersecurity.
As you can see, this is an impressive series of releases and one that addresses some of the hottest trends in modern ML applications. When it comes to ML, Microsoft continues to innovate at a very impressive pace and it's becoming one of the most complete suites of ML technologies in the market. Edge#69: search strategies in neural architecture search; Google's evolved transformer that is a killer combination of transformers and NAS; Microsoft's neural network intelligence -- the most impressive AutoML framework you have ever heard of.
Artificial intelligence is becoming good at many "human" jobs--diagnosing disease, translating languages, providing customer service--and it's improving fast. This is raising reasonable fears that AI will ultimately replace human workers throughout the economy. Never before have digital tools been so responsive to us, nor we to our tools. While AI will radically alter how work gets done and who does it, the technology's larger impact will be in complementing and augmenting human capabilities, not replacing them. Certainly, many companies have used AI to automate processes, but those that deploy it mainly to displace employees will see only short-term productivity gains. In our research involving 1,500 companies, we found that firms achieve the most significant performance improvements when humans and machines work together. Through such collaborative intelligence, humans and AI actively enhance each other's complementary strengths: the leadership, teamwork, creativity, and social skills of the former, and the speed, scalability, and quantitative capabilities of the latter. What comes naturally to people (making a joke, for example) can be tricky for machines, and what's straightforward for machines (analyzing gigabytes of data) remains virtually impossible for humans.
While smart cities and smart homes have become mainstream buzzwords, few people outside the IT and machine learning communities know about TensorFlow, PyTorch, or Theano. These are the open-source machine learning (ML) frameworks on which smart systems are built to integrate Internet of Things (IoT) devices among other things. ML algorithms and code are often found in publically available repositories, or data stores, that draw heavily on the aforementioned frameworks. In a December 2019 analysis of code hosting site GitHub, SMU Professor of Information Systems David Lo found over 46,000 repositories that were dependent on TensorFlow, and over 15,000 used PyTorch. Because of these frameworks' popularity, any vulnerability in them can be exposed to cause widespread damage.
The past decade- the 2010s- was truly a decade of startups. Indeed, lots of successful startups are changing the world over the last 10 years. Analytics Jobs has brought you another story of a startup that enables Data Science and Artificial Intelligence to accelerate the discovery of drugs. The risks of cybersecurity are more advanced than ever before. Data is among the key features of every organization since it can help business leaders to make choices based on facts and figures, statistical numbers & trends.