UrbannFox

is a day to review your defenses. If any of these passwords reminds you of your pin you might want to consider changing it. 🔐

𝟱 𝗗𝗮𝗻𝗴𝗲𝗿𝘀 𝗼𝗳 𝗔𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗶𝗻 𝘁𝗵𝗲 𝗙𝘂𝘁𝘂𝗿𝗲

Artificial Intelligence is a great tool for development currently. It has revolutionized technology in all industries and solved many problems faced by humanity. But AI is still in its beginning phases and it can also lead to great harm if it is not managed properly. There are many areas in which Artificial Intelligence can pose a danger to human beings and it is best if these dangers are discussed now so that they can be anticipated and managed in the future.

1. Invasion of Privacy
2. Autonomous Weapons
3. Loss of Human Jobs
4. Artificial Intelligence Terrorism
5. Artificial Intelligence Bias

𝗙𝗮𝗰𝗲𝗯𝗼𝗼𝗸’𝘀 𝘁𝗲𝗮𝗰𝗵𝗶𝗻𝗴 𝗔𝗜 𝘁𝗼 𝗹𝗶𝗲 𝗹𝗶𝗸𝗲 𝗮 𝗵𝘂𝗺𝗮𝗻

Facebook AI researchers today announced Blender, a set of state-of-the-art open-source chatbot models. These new models are supposed to be “more human” than previous iterations and provide more robust conversations.

Based on early indications it appears to be as good as or better than competing bots from Google and Microsoft. But we’re not talking leaps and bounds by any measure. Because, essentially, what Facebook‘s done is taught Blender’s chatbots to lie moderately better than everyone else’s.

𝗪𝗵𝗮𝘁 𝗶𝘀 𝗮 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸❓
A neural network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. In this sense, neural networks refer to systems of neurons, either organic or artificial in nature. Neural networks can adapt to changing input; so the network generates the best possible result without needing to redesign the output criteria. The concept of neural networks, which has its roots in 𝗮𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲, is swiftly gaining popularity in the development of 𝘁𝗿𝗮𝗱𝗶𝗻𝗴 𝘀𝘆𝘀𝘁𝗲𝗺.

Sometimes you don’t notice something until it’s gone. Such is one stark visual lesson of the pandemic: air pollution is something way too many people around the world breathe in every day.

The shutdowns of business and travel during the Covid-19 pandemic have cleared the air in cities as pollution has fallen sharply. China, for instance, experienced a 40 percent decline in nitrogen dioxide, a pollutant produced from burning fossil fuels in cars and power plants, earlier this year compared to the same period last year.

Air pollution is a deadly threat, killing millions around the world every year. It’s also linked to more severe outcomes for Covid-19. Conversely, the economic slowdown stemming from Covid-19 showed that reducing pollution yields massive benefits for public health. One researcher estimated that the drop in air pollution in China saved 20 times as many lives as were lost to the virus.

And China isn’t the only place seeing clearer skies. In the United Kingdom, nitrogen dioxide pollution fell 60 percent after the country implemented a lockdown compared to the year prior. A nationwide lockdown in India revealed skylines and mountain vistas that had been obscured for decades.

The Covid-19 pandemic has shown how quickly air quality can improve. But it doesn’t mean we should celebrate the pandemic for its impact on the environment. The clearer skies came at a devastating social and economic cost. The challenge for countries then is to reduce pollution without a massive toll.

Countries like China and the United States are weakening environmental regulations in the hope of helping industries better recover from the slowdown, so the pollution could come back with a vengeance. But it’s worth asking why we tolerated so much dirty air for the sake of the economy for so long and whether there’s a better way. @ Pune, Maharashtra

In the past few years, careers in AI have peaked to cater to the growing demands of digitally transformed industries. According to a 2018 report by Indeed, the need for AI skills has more than doubled in the past three years, with job postings in the domain going up by a whopping 119%.

Unfortunately, there are not enough talented professionals in Data Science (AI and ML) who can fill these job vacancies. According to a study by Analytics India Magazine in collaboration with Great Learning, “over 4,000 positions in India remain vacant due to shortage of qualified talent at the mid and senior level.”