Here’s Why AI May Be Extremely Dangerous

 Artificial intelligence algorithms will soon reach a point of rapid self-improvement that threatens our ability to control them and poses great potential risk to humanity

“The idea that this stuff could actually get smarter than people.... I thought it was way off…. Obviously, I no longer think that,” Geoffrey Hinton, one of Google's top artificial intelligence scientists, also known as “the godfather of AI,” said after he quit his job in April so that he can warn about the dangers of this technology.

He’s not the only one worried. A 2023 survey of AI experts found that 36 percent fear that AI development may result in a “nuclear-level catastrophe.” Almost 28,000 people have signed on to an open letter written by the Future of Life Institute, including Steve Wozniak, Elon Musk, the CEOs of several AI companies and many other prominent technologists, asking for a six-month pause or a moratorium on new advanced AI development.

As a researcher in consciousness, I share these strong concerns about the rapid development of AI, and I am a co-signer of the Future of Life open letter.

Why are we all so concerned? In short: AI development is going way too fast.

The key issue is the profoundly rapid improvement in conversing among the new crop of advanced "chatbots," or what are technically called “large language models” (LLMs). With this coming “AI explosion,” we will probably have just one chance to get this right.

If we get it wrong, we may not live to tell the tale. This is not hyperbole.

This rapid acceleration promises to soon result in “artificial general intelligence” (AGI), and when that happens, AI will be able to improve itself with no human intervention. It will do this in the same way that, for example, Google’s AlphaZero AI learned how to play chess better than even the very best human or other AI chess players in just nine hours from when it was first turned on. It achieved this feat by playing itself millions of times over.

A team of Microsoft researchers analyzing OpenAI’s GPT-4, which I think is the best of the new advanced chatbots currently available, said it had, "sparks of advanced general intelligence" in a new preprint paper.

In testing GPT-4, it performed better than 90 percent of human test takers on the Uniform Bar Exam, a standardized test used to certify lawyers for practice in many states. That figure was up from just 10 percent in the previous GPT-3.5 version, which was trained on a smaller data set. They found similar improvements in dozens of other standardized tests.

Most of these tests are tests of reasoning. This is the main reason why Bubeck and his team concluded that GPT-4 “could reasonably be viewed as an early (yet still incomplete) version of an artificial general intelligence (AGI) system.”

This pace of change is why Hinton told the New York Times: "Look at how it was five years ago and how it is now. Take the difference and propagate it forwards. That’s scary.” In a mid-May Senate hearing on the potential of AI, Sam Altman, the head of OpenAI called regulation “crucial.”

Once AI can improve itself, which may be not more than a few years away, and could in fact already be here now, we have no way of knowing what the AI will do or how we can control it. This is because superintelligent AI (which by definition can surpass humans in a broad range of activities) will—and this is what I worry about the most—be able to run circles around programmers and any other human by manipulating humans to do its will; it will also have the capacity to act in the virtual world through its electronic connections, and to act in the physical world through robot bodies.

This is known as the “control problem” or the “alignment problem” (see philosopher Nick Bostrom’s book Superintelligence for a good overview) and has been studied and argued about by philosophers and scientists, such as Bostrom, Seth Baum and Eliezer Yudkowsky, for decades now.

I think of it this way: Why would we expect a newborn baby to beat a grandmaster in chess? We wouldn’t. Similarly, why would we expect to be able to control superintelligent AI systems? (No, we won’t be able to simply hit the off switch, because superintelligent AI will have thought of every possible way that we might do that and taken actions to prevent being shut off.)

Here’s another way of looking at it: a superintelligent AI will be able to do in about one second what it would take a team of 100 human software engineers a year or more to complete. Or pick any task, like designing a new advanced airplane or weapon system, and superintelligent AI could do this in about a second.

Once AI systems are built into robots, they will be able to act in the real world, rather than only the virtual (electronic) world, with the same degree of superintelligence, and will of course be able to replicate and improve themselves at a superhuman pace.

Any defenses or protections we attempt to build into these AI “gods,” on their way toward godhood, will be anticipated and neutralized with ease by the AI once it reaches superintelligence status. This is what it means to be superintelligent.

We won’t be able to control them because anything we think of, they will have already thought of, a million times faster than us. Any defenses we’ve built in will be undone, like Gulliver throwing off the tiny strands the Lilliputians used to try and restrain him.

Some argue that these LLMs are just automation machines with zero consciousness, the implication being that if they’re not conscious they have less chance of breaking free from their programming. Even if these language models, now or in the future, aren’t at all conscious, this doesn’t matter. For the record, I agree that it’s unlikely that they have any actual consciousness at this juncture—though I remain open to new facts as they come in.

Regardless, a nuclear bomb can kill millions without any consciousness whatsoever. In the same way, AI could kill millions with zero consciousness, in a myriad ways, including potentially use of nuclear bombs either directly (much less likely) or through manipulated human intermediaries (more likely).

So, the debates about consciousness and AI really don’t figure very much into the debates about AI safety.

Yes, language models based on GPT-4 and many other models are already circulating widely. But the moratorium being called for is to stop development of any new models more powerful than 4.0—and this can be enforced, with force if required. Training these more powerful models requires massive server farms and energy. They can be shut down.

My ethical compass tells me that it is very unwise to create these systems when we know already we won’t be able to control them, even in the relatively near future. Discernment is knowing when to pull back from the edge. Now is that time.

ISRO's Second Anticipated Mission To The Sun : Misson Aditya L1

 After The Successful Launch Of Chandrayaan 3 ISRO Is Now Preparing For Its Second Much Anticipated  Mission 

India's first solar mission, ISRO's Aditya L1 spacecraft, named after one of the Sanskrit names of the Sun, will be launched by the PSLV-XL launch vehicle on August 26, 2023, from the Satish Dhawan Space Centre (SDSC SHAR) in Sriharikota. It will be launched into Low Earth Orbit (LEO) around 800 km from the Earth's surface. The spacecraft will perform a 'Surya Namaskar' (salutation to the Sun) as it orbits around a special point called Lagrange point 1 (L1), which is about 93 million miles or 149 million kilometers away from the Sun.

This mission will be the first of its kind to study and research the Sun's atmosphere, its environment, and everything related to it. The 1,500 kg spacecraft will carry special equipment called payloads to study different parts of the Sun's atmosphere.

The mission is expected to be launched in august. this will be india's first mission to study sun 

Out of the seven payloads the satellite carries to study different parts of the Sun, including its outer layer (the corona), four look directly at the Sun from a special position called L1, while the other three study particles and fields at the same point. These studies help us learn about how solar activity affects the space between planets. The Aditya L1 payloads' suits are expected to provide crucial information about various aspects of the Sun, like coronal heating, coronal mass ejections, pre-flare and flare activities, space weather dynamics, and the movement of particles and fields,” explained Girish Linganna, Space and Aerospace expert.

The L1 point is one of the most important Lagrangian points, found by mathematician Joseph Louis Lagrange. It is located about 1.5 million kilometers inside Earth's orbit, between the Sun and the Earth. “At these points, the gravitational forces between two objects balance out or have a neutral gravity point, making it possible for spacecraft to stay in one place without using much fuel. These spots are like parking spaces in space that spacecraft can use to stay in one place without using much fuel. It is like finding a stable spot in a river where the water flows in a way that keeps you in the same spot without paddling,” added Linganna.

According to Linganna, out of the five Lagrange points, three are not very steady, and two are stable. The unstable ones, called L1, L2, and L3, are found along the line connecting the two big masses. The stable ones, known as L4 and L5, form the tips of two equal-sided triangles with big masses at their corners. L4 is ahead of Earth's orbit, and L5 is behind it.

“The L1 point of the Earth-Sun system gives a clear view of the sun all the time, without any occultation/ eclipses and it is where the Solar and Heliospheric Observatory Satellite (SOHO) is located,” explained Linganna.

Aditya L1, is also known as Aditya-Lagrange Point 1. After reaching the LEO, the satellite will be maneuvred using its onboard thrusters. “A series of earth burn elliptical orbital maneuvers is conducted to raise its orbit towards the L1 Lagrange point to surpass the earth's gravitational pull. The estimated time required to reach the L1 Lagrange point is around 109 days,” said Dr. T.N. Suresh Kumar, former senior scientist at ISRO.

Once the Aditya L1 mission reaches the L1 Lagrange point, it will be injected to a halo orbit. “A halo orbit is a type of orbit that allows the satellite to remain in a stable position between the Earth and the Sun. The satellite will use its onboard scientific instruments to study the dynamics of Sun's chromosphere and corona, its magnetic field, and its solar flares, solar wind etc. The data collected by the Aditya L1 mission will help scientists to understand better about the Sun and its impact on Earth,” Kumar told THE WEEK.

The Aditya L1 mission will use its onboard thrusters to increase its orbital height and exit from the earth's gravitational influence, then it will cruise on a coasting path towards L1. This method is adopted to have the best propulsion efficiency to conserve the propellant. Once the spacecraft reaches a distance of approximately 1.5 million kilometers from Earth, it will be inserted into a halo orbit.
The onboard thrusters on the Aditya spacecraft are small rocket engines that use its onboard computer to control the spacecraft's attitude and orbit till the end of life of the mission. The Aditya L1 mission will always be in reduced Earth gravitational pull, as it will be located in the L1 Lagrange point. The L1, L2, and L3 Lagrange points are colinear, meaning that they lie along the line connecting the Earth and the Sun. The L4 and L5 Lagrange points are 60 degrees apart, located 60 degrees ahead and behind the Earth in its orbit around the Sun.

“The Aditya L1 mission is a complex and challenging task, but it has the potential to provide valuable insights into the Sun and its impact on Earth. The mission is expected to have a life of around 4 to 5 years,” said Kumar. 

After Moon, Comes The Sun!🔆

PROUD TO BE INDIAN

Why Is 3 am Called The Devil's Hour?

 

Often we wake up in the night feeling all confused, sweaty and jittery. However, if at this time we look at the watch and it is 3am, it sends our mind into a tizzy! Often we have heard that this hour is known as the Devil’s hour. Here why.

Content.

1.Why is it called so?

2.Between 3-4am

3.The crucification of Jesus

4.Sacrifices and rituals

5.Going to bed

6.Movies and more

7.The reality

1. Why is it called so?

It is said that the demon or devil who leads hell is at his strongest at this hour of the day.

2. Between 3-4am

It is said that while demonic activities are highest at 3am, the preceding hour (3am-4am), too is demonic before sunlight peeps in some parts of the world.

3. The crucification of Jesus

It is believed that Jesus was crucified at 3pm (which in turn is now known as the most "godly" hour ) and exactly 12 hours later that is at 3am, the demonic activities set in. In hindsight, while demons are weakest at 3pm, they are strongest at 3am.

4. Sacrifices and rituals

It is also said that 3am is also the time when maximum rituals and sacrifices take place - apparently the fact that "nobody" can watch them makes them more powerful.

5. Going to bed

It is also said that if by chance you wake up at 3am someday, you should try to go to bed immediately - one should not wait for any untoward thing to happen

6. Movies and more

3am is also seen as a very unholy time in movies. If you have seen The Conjuring, the clock stops at 3 in the morning and everything goes downhill from there. Similarly in The Exorcism of Emily Rose where Emily finds herself waking up to a strange smell at 3am everyday.

7. The Reality

The Mythology of the Witching Hour and why 3 am is known as the devil’s hours. 3am the witching hour, also known as the devil’s hour, is the time of night when many cultures believe that supernatural creatures, such as witches and ghosts, come out of hiding and roam the world.

It is believed to occur between the hours of 3am and 4am, though some cultures place the start time at 2am.

The belief in a witching hour originates from European folklore, with many stories involving witches and ghosts during this time.

In some cultures, such as in Ireland, it is believed that the devil visits people in their sleep during this hour. Some people believe that during this hour, the veil between this world and the afterlife is thinner, allowing for more communication between the two planes.

The idea of the 3am the Witching Hour has become a popular theme in media, particularly in horror films and literature.

The idea of a witching hour is used to create a sense of fear and dread in stories, as it often involves the unknown and mysterious, leaving the audience guessing as to what will happen next.

In modern times, the witching hour has become less feared, though some people still place importance on the time of night, avoiding certain activities and staying inside during the 3am the Witching Hour.

Many people also use the time of night to meditate, as they believe that during this hour the spirit world is more accessible.

Whether or not the witching hour is real is still up for debate, but one thing is for sure: the belief in the witching hour has been around for centuries and will continue to fascinate and terrify people for years to come.

Unraveling The Bloody Tales Of Shaniwar Wada

Pune takes great pride in its rich legacy and heritage. The 286-year-old Shaniwar Wada fort is one of the most renowned and prestigious forts in the region, and it is a reminder of the proud Maratha Empire. It was once a melting pot of culture and politics, and it now draws nearly 300 tourists from all over the world every day. Peshwa Bajirao I built the Wada fortification in Pune, which covers approximately 625 acres of land, as the Peshwas’ residence. It is a significant site in Indian history, but it has been nearly destroyed by several military attacks and fires in the 18th and 19th centuries, and it is in disrepair. The once most magnificent mansion, which had seven stories, fortification walls with gates, court halls, fountains, and reservoirs, is now in ruins. 

The fort was once Baji Rao and Kashi’s love nest, built right after their marriage. It was constructed in 1732 on 625 arches of land and served as the Maratha Empire’s capital building. We are all familiar with Bajirao Masatani’s love story. One Can Book A Cab In Pune to explore the legacy of their love story in Shaniwar Wada. However, this has not only witnessed an iconic love story; it has also been the site of political conspiracy, betrayal, family bitterness, and brutal murder.

The fort that witnessed a prince’s murder

Aside from its natural beauty, the palace is thought to have been the site of one of the most brutal murders in Maratha Empire history. It all began in 1732, when Shaniwar Wada witnessed the untimely death of Peshwa Bajirao I. Then Bajirao’s elder son, Nanasaheb, asserted his authority. After Nanasaheb’s death, his elder son Vishwasrao took over, which enraged Raghunathrao, Bajirao’s younger son and Nanasaheb’s brother, who was expecting the throne.

Vishwasrao died in the third battle of Panipat against Afghan armies, and Nanasaheb Madhavrao became the fourth Peshwa of the Maratha Empire. He was only 17 years old when his brother Madhavrao died, making him the youngest Peshwa ruler ever. Because he wasn’t yet an adult, his uncle Raghunathrao was in charge of the state on his nephew’s behalf. Anandibai, Raghunathrao’s wife, was jealous because she desired the throne for her husband. As a result, she sought the assistance of the Gardis, highly trained assassins for hire. Gardis were originally a hunting tribe from central India.

On the eve of Ganesh Chaturthi, assassins entered Shaniwarwada to assassinate Narayanrao. Despite his attempts to flee for his life, the merciless Gardi assassins mercilessly killed him, tearing him to shreds. His mutilated remains were then dumped into the river. Narayan Rao’s death is the bloodiest chapter in Indian history, bringing dishonour to Peshwa Bajirao I’s proud legacy. It is said that Narayan Rao screamed for help to his uncle, ‘Kaka maala vachva’ (Save me uncle), but his crafty uncle Raghoba ignored his pleas. Because Narayan Rao’s mortal remains were never cremated according to Hindu tradition, he is thought to be roaming as a phantom figure in Shaniwarwada.

Shaniwar Wada’s final tragedy

The British won a decisive victory in the Third Anglo-Maratha War under Peshwa Baji Rao II, capturing Puna and, with it, Shaniwar Wada in 1818. Shaniwar Wada became a hollow shell after all of the Peshwa’s territories were annexed. The British East India Company began to use it for a variety of purposes, including a prison, a hospital, and even a mental asylum. Approximately ten years after this event, the entire palace was completely burned down by a great fire on February 27, 1828, which lasted for seven days, and nothing of this magnificent building has been saved from the cruel hand of time except the heavy ramparts, strong gateways, and buried foundations and ruins that still bear witness to the rise and fall of a mighty Empire.

The Fort With A Ghost 

Almost everyone in Pune is aware of the legend that this historical monument is haunted. Paranormal activity is said to occur within the walls of this fort on the night of every new moon. The site of Narayanrao’s murder is said to be haunted by his spirit, which moves around the fort pleading with his uncle to save him. People who live nearby claim to have heard his cries on specific nights. After 6:30 p.m., entry to the fort is strictly prohibited. 

This Story Is Not Meant To Scare You, It Is Just To Let You Know Something New

 -Story Own By Shripad Sunil Chaudhari 

Is Time Travel Possible?

 We all travel in time! We travel one year in time between birthdays, for example. And we are all traveling in time at approximately the same speed: 1 second per second.

NASA's space telescopes also give us a way to look back in time. Telescopes help us see stars and galaxies that are very far away. It takes a long time for the light from faraway galaxies to reach us. So, when we look into the sky with a telescope, we are seeing what those stars and galaxies looked like a very long time ago.

However, when we think of the phrase "time travel," we are usually thinking of traveling faster than 1 second per second. That kind of time travel sounds like something you'd only see in movies or science fiction books. Could it be real? Science says yes!

How do we know that time travel is possible?

More than 100 years ago, a famous scientist named Albert Einstein came up with an idea about how time works. He called it relativity. This theory says that time and space are linked together. Einstein also said our universe has a speed limit: nothing can travel faster than the speed of light (186,000 miles per second).

What does this mean for time travel? Well, according to this theory, the faster you travel, the slower you experience time. Scientists have done some experiments to show that this is true.

For example, there was an experiment that used two clocks set to the exact same time. One clock stayed on Earth, while the other flew in an airplane (going in the same direction Earth rotates).

After the airplane flew around the world, scientists compared the two clocks. The clock on the fast-moving airplane was slightly behind the clock on the ground. So, the clock on the airplane was traveling slightly slower in time than 1 second per second.

Can we use time travel in everyday life?

We can't use a time machine to travel hundreds of years into the past or future. That kind of time travel only happens in books and movies. But the math of time travel does affect the things we use every day.

For example, we use GPS satellites to help us figure out how to get to new places. (Check out our video about how GPS satellites work.) NASA scientists also use a high-accuracy version of GPS to keep track of where satellites are in space. But did you know that GPS relies on time-travel calculations to help you get around town?

GPS satellites orbit around Earth very quickly at about 8,700 miles (14,000 kilometers) per hour. This slows down GPS satellite clocks by a small fraction of a second (similar to the airplane example above).

Data Scientist the Sexiest Job of the 21st Century?

 The role was relatively new at the time, but as more companies attempted to make sense of big data, they realized they needed people who could combine programming, analytics, and experimentation skills. At the time, that demand was largely restricted to the San Francisco Bay Area and a few other coastal cities. Startups and tech firms in those areas seemed to want all the data scientists they could hire. We felt that the need would expand as mainstream companies embraced both business analytics and new forms and volumes of data.

At the time, we defined the data scientist as “a high-ranking professional with the training and curiosity to make discoveries in the world of big data.” Companies were beginning to analyze voluminous and less-structured data like online clickstreams, social media, and images and speech. Because there wasn’t yet a well-defined career path for people who could program with and analyze such data, data scientists had diverse educational backgrounds. The most common qualification in our informal survey of 35 data scientists at the time was a PhD in experimental physics, but we also found astronomers, psychologists, and meteorologists. Most had PhDs in some scientific field, were exceptional at math, and knew how to code. Given the absence of tools and processes at the time to perform their roles, they were also good at experimentation and invention. It’s not that a science PhD was really required to do the work, but rather that these individuals had the rare ability to unlock the potential of data, wading through complex, messy data sets and building recommendation algorithms.

A decade later, the job is more in demand than ever with employers and recruiters. AI is  increasingly popular in business, and companies of all sizes and locations feel they need data scientists to develop AI models. By 2019, postings for data scientists on Indeed had risen by 256%, and the U.S. Bureau of Labor Statistics, predicts data science will see more growth than almost any other field between now and 2029. The sought-after job is generally paid quite well; the median salary for an experienced data scientist in California is approaching $200,000.

Many of the same headaches remain, too. In our research for the original article, many data scientists noted that they spend much of their time cleaning and wrangling data, and that is still the case despite a few advances in using AI itself for data management improvements. In addition, many organizations don’t have data-driven cultures and don’t take advantage of the insights provided by data scientists. Being hired and paid well doesn’t mean that data scientists will be able to make a difference for their employers. As a result, many are frustrated, leading to high turnover.

Even so, the job has changed — in both large and small ways. It’s become better institutionalized, its scope has been redefined, the technology it relies on has made huge strides, and the importance of non-technical expertise, such as ethics and change management, has grown. The many executives who recognize that data science is important to their businesses now need to create and oversee diverse data science teams rather than searching for data scientist unicorns. They can also begin to think about democratizing data science — still with the aid of data scientists, however.                                                                                                                                

Data Scientists in Relation to Other Roles

The data science role is also now supplemented with a variety of other jobs. The assumption in 2012 was that data scientists could do all required tasks in a data science application — from conceptualizing the use case, to interfacing with business and technology stakeholders, to developing the algorithm and deploying it into production. Now, however, there has been a proliferation of related jobs to handle many of those tasks, including machine learning engineer, data engineer, AI specialist, analytics and AI translators, and data oriented product managers. LinkedIn reported some of these jobs as being more popular than data scientists in its “Jobs on the Rise” reports for 2021 and 2022 for the U.S.

Part of the proliferation is due to the fact that no single job incumbent can possess all the skills needed to successfully deploy a complex AI or analytics system. There is an increasing recognition that many algorithms are never deployed, which has led many organizations to try to improve deployment rates. Additionally, the challenges of managing increased data systems and technologies have resulted in a more complex technical environment. There have been some attempts at certification of data scientists and related jobs, but these are not yet widely sought or recognized. Some companies, like TD Bank, have developed classification structures for the many data science-related careers and skills, but these are not common enough in organizations.

As a result of this proliferation of skills, companies need to identify all of the different roles required to effectively deploy data science models in their businesses, and ensure that they are present and collaborating on teams.