Good morning, good evening and good night! Good morning to people who read the article on how Vitalik Buterin uses the ChatGPT to solve his problems. Good evening to traders who are excited by the news that Arbitrum (ARB) rose 28% as the Biggest Beneficiary of Shapella Upgrade. And of course, good night to users and programmers who have already joined the network using Metamask and receive test WBTs, because WhiteBIT crypto exchange launched a test net of its own blockchain.

The history period from the beginning of stock exchanges to AI technologies is longer than even the timelines of the TV show Dynasty. Telling about the beginning of development of the stock market, I always use the timeline from the SoFi Learn, because if I were to write about each period of changes, it has to be the article on 10 pages.

I know that it’s not a history lesson at school. That’s why I prefer to write about what interests not only financiers and economists, but also ordinary people who watched movies about the Wall-Street, in the center of the Universe — New York City.

It took centuries for the New York Stock Exchange to reach its current state. The Buttonwood traders created the New York Stock and Exchange Board in 1817 after visiting and observing the Philadelphia Merchants Exchange. The members have to enter the exchange and adhere to a dress code. Additionally, they had to pay a charge, which by 1837 had gone from $25 to $100. Lower Manhattan lost 700 buildings in the Great Fire of 1835, while Wall Street sustained substantial property losses. Fortunately, Samuel Morse established a telegraph demonstration office, enabling remote communication between brokerages.

The NYSE opened its doors in 1903 with thousands of stock certificates stored in underground vaults. Despite signs of an economic collapse, the stock market soared and reached a 50% high in 1928. Black Thursday, a day when the market fell 11% in 1929, is referred to as such. Investors panic as the market falls, and it takes the whole 1930s to recover from the disaster. The Great Depression was in effect during this time.

Several additional disasters have occurred since then, most notably the subprime mortgage crash in 2008. Even though the NYSE was founded by a small group of merchants centuries ago, several investors, exchange executives, businesses, and regulators have helped it develop into what it is today.

After the world-wide mortgage crash in 2008, the world had known about the new objective that became the topic for discussions, disputes and scandals, of course to the present days. Similar to most things in life, when something new is introduced to the market, it doesn’t take long for it to be imitated. This was also true of Bitcoin. Rival cryptocurrencies began to enter the market in 2011, with the likes of Litecoin, Namecoin, and Swiftcoin to mention a few. This is hardly surprising given that the market value of the Bitcoin currency is currently a staggering $44 billion. As a result, software developers all over the world are constantly producing new cryptocurrencies in an effort to outdo Bitcoin as the next big thing.

Despite the thousands of active cryptocurrencies experiencing the highest value increases in 2017, they have yet to significantly impact our day-to-day lives. The majority of people who hold sizable quantities of Bitcoin do so as an investment rather than as a new method of making online purchases. Immediately following the unheard-of surge in 2017, the start of 2018 saw a different story. Market novices are uncertain whether the market would ever rebound after it plummeted and plunged by 65%.

Any automated procedure that creates, modifies, or synthesizes data using algorithms, frequently in the form of images or text that can be read by humans, is referred to as generative AI. Because the AI develops something that didn’t previously exist, it is considered generative. It differs from discriminative AI, which makes distinctions between various input types, in that regard. To put it another way, generative AI reacts to requests like “Draw me a picture of a lion and a rabbit sitting next to each other,” but discriminative AI tries to determine whether an image is a drawing of a rabbit or a lion.

Phipps’ argument will be largely clear when we give these AI models specific instructions. Take the question, “What weighs more, a pound of lead or a pound of feathers?” as an example. Even if our instinct or common sense would lead us to believe that the feathers are lighter, the obvious explanation is that they both weigh the same (one pound).

You would believe that ChatGPT will provide the correct response to this puzzle because it is a coldly logical machine without any “common sense” to confuse it. But that’s not what’s happening behind the scenes. ChatGPT simply generates output based on its predictions of what should happen after a question about a pound of feathers and a pound of lead; it does not logically reason out the solution. Since its training set contains a ton of text outlining the puzzle, it puts together a rendition of the right response.

Even while AI is still in its infancy, its promise has captured the attention of commercial and political leaders in addition to scientists and philosophers. The rationale is straightforward: AI will grow into a sizable industry that opens up a flood of business opportunities and gives industry leaders, including governments and corporations, unmatched technological power.

Regardless of the shape AI takes, its development will be riddled with ethical quirks and greeted, frequently simultaneously, with fear and joy. Some people will be concerned about job losses, privacy, and control, while others will be excited about the next development in human greatness. Whatever your opinion, AI will likely impact us and our world in a variety of ways, so it’s critical to be ready for what lies ahead. In conclusion, the main purpose of humanity right now is helping to promote new technologies, not confront them.