This blog discusses the term "artificial
intelligence" (AI), its evolution, and its impacts on various markets. The
term was first coined in 1955, and over the years, AI has grown to have
significant influence in different sectors. For instance, there has been a
decrease in writing, customer service, and translation jobs due to AI. However,
fields like web designing and video editing have increased demand. In
the stock market, companies showing potential benefits from AI have seen
growth, like NVIDIA, SAP, Microsoft, and Salesforce.
The blog also discusses GPT (Generative Pre-training
Transformer) and how it reached 100 million users in just two months. The blog
states that the growth of AI contributes significantly to global GDP due to
increased efficiency in different work processes.
The evolution of AI runs parallel with the growth of data
and computing power. The emergence of the internet in the early 70s and its
transition from Web 2.0, where individuals create their websites, to Web 3.0, where
we have Amazon and Netflix, has led to an enormous increase in data.
With the emergence of social media and cloud computing,
users have generated even more data, providing valuable
information for AI development. Gaming company NVIDIA introduced a GPU
processing unit in 2010, boosting computing power and accelerating machine
learning concepts.
Despite AI's remarkable growth, the blog emphasizes challenges, particularly job losses in some sectors. However, the
potential for efficiency and economic development with AI is vast and continuing to
increase.
Over the past decade, machine learning has revolutionized
our world. A new realm of concepts was introduced with various forms, such as supervised, unsupervised, and
reinforcement learning, as well as the development of neural networks. However, the complexity and cost of training
data was a significant obstacle. This changed in 2017 when the Google Deep
Brain team launched a new framework called 'all the attention you need', which
drastically reduced the cost and manpower of training data.
In generative AI, models like GPT have three
elements – generative, pre-trained, and transformer. The generative model can
create new words, images, and videos, while the pre-trained component is trained
automatically without human intervention. The transformer model helps encode and decode commands. Generative AI's capability to create new and
original data propels a comet of change across various industries.
Applications range from chatbots increasing personal and organizational
efficiency to language translation.
Despite the immense benefits, there are ethical
considerations and potential downsides. The most significant concern involves
bias issues, which can creep into the AI system. There's also the risk of
unethical data usage and environmental impacts due to high energy consumption.
Despite these challenges, the future for AI is promising and vast, with
continuing advancements and applications in various fields such as image
generation and data analysis.
The training of AI models involves pulling a vast data set
from the internet, including publications, social media, programming codes from
GitHub, and more. The model is then trained and tested by masking parts of the
data and predicting what's missing, creating a "pretrained" model or
"brain". Lastly, ongoing input and feedback cause a continuous
evolution and fine-tuning of the AI's parameters, making it progressively more
intelligent, almost like a human brain.
Despite all the advancements, it's still a work in progress, and AI still needs to be at the level of human intelligence. However, there's
considerable hope and excitement for a future where AI's ability to understand
and process information will surpass human capabilities.
In the artificial intelligence (AI) world, it's crucial
to stay relevant and effectively adapt our work processes. With AI capable of
performing an increasing number of tasks, it's vital to assess whether AI can complete jobs or if they require a human-based approach. Tasks related to
relationship management or conflict resolution remain human activities,
whereas AI is ideal for functions centred around generating issues or classifying
metadata.
To maximize AI's potential, we must learn prompt
engineering, enabling us to describe our work to AI accurately. While it's
predicted that prompt engineering will likely become redundant with the advent
of new websites that can write prompts, it's currently an essential AI skill.
Equally crucial is continuous learning, adapting ourselves to the rapidly
developing AI technologies in the market.
Several high-quality resources, such as the AI podcast Discover Daily, offer invaluable insights into the latest
developments in AI. They keep us updated with AI-related news and offer tips
and valuable analyses. You can also follow my Newsletter: TechSambad
The shift from AI towards Artificial General Intelligence
(AGI) raises crucial questions about its implications. AGI refers to increasingly intelligent systems capable of learning and improving over time.
While AGI's capabilities are not fully realized yet, witnessing AI's increasing
influence across various sectors suggests that AGI's arrival maybe sooner than
we think. Staying up-to-date with these dramatic shifts, continuously learning
and growing, is paramount.
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