The invention of the wheel was an illusion; the real genius was the axle. Regardless, the concept prepared the grounds for an entire epoch spanning millennia.
The next significant invention was the personal computer – the bicycle for the mind. That, followed by a string of software-propelled gizmos, has never let the human race look back.
The software on these devices seemed to get smarter. All that prepared us for the intelligence race. Organizations like DeepMind and OpenAI are locked in an asymmetrical race, each in the pursuit of developing related but varied software tools with increasingly intelligent algorithms.
There’s no question about the extent to which software is turning smarter. Take the algorithms running search engines today; with a multitude of mini programs and machine learning neural networks of various kinds, they absorb all sorts of patterns millions of people reveal to them just by daily use.
But this is a vast oversimplification of what this all entails just for an immediate understanding. It is much more than this.
And the best part is that the competing organizations are the fuel necessary to take the human race to the forefront of our fullest human potential. So, comparing OpenAI vs. DeepMind will help us learn much more about the direction in which we are heading.
So, let’s start here; what’s more convincing than seeing your work done automatically without worrying too much about it?
All thanks to AI for incredible happenings that we watch over the internet and see things getting done around the globe.
Talking about AI and its revolutionary impact on the world is all cool and sounds great until we imagine our job getting done within no time by AI-powered software. Sound scary?
However, AI is the catalyst of the technology that’s leading the world at a rapid pace. AI-based solutions are gearing up companies across several industries and powering up their processes with intelligent, customized solutions.
It is clearly seen that AI tools that gather information, analyze it and provide better and more feasible solutions will eventually get smarter. However, despite ongoing innovations, everyone wants to debate AI powers and particularly the potential of the GPT-3 launch.
AI vs. AGI
AI-powered large language models have become more popular in the present. However, it is seen that the tech industry is juggling between AI and AGI (artificial general intelligence). The new tech (particularly AI-based multi-modal) advancement has raised some serious issues about whether or not we could ever achieve AGI.
Why? Because OpenAI and DeepMind tried to achieve AGI through their respective models but failed to do so, as AGI requires such models to self-learn and adopt new concepts regardless of training data.
AGI shows great human cognitive abilities when an unfamiliar task is assigned and finds a solution. Therefore, it learns independently like humans do. So, AGI expects the machine to carry out work independently and in a more effective way.
Whereas AI is a technology that makes the software or machine copy human cognitive abilities, AI-powered software requires pre-programmed data to create new information or provide solutions to any problem.
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OpenAI and its latest product, GPT-3, are the current market sensation and the most popular buzzword in the tech realm. This multi-modal can produce long detailed content, generate code, and answer your queries.
Users seem to be more inclined towards asking the chatbot to write and code. The model has an increased capacity and considerably many more parameters. It was trained on unlimited words and can also code in CSS, Python, and many other languages. OpenAI creates it, and it only needs a brief description of your requirements in text forms and produces detailed machine-generated content with accuracy.
With over 175 billion ML parameters, OpenAI’s multi-modal is currently the largest trained language model and therefore is considered efficient in producing text that seems like produced by a human.
Moreover, GPT-3 can answer users’ questions with mind-blowing accuracy. In addition, with smart-human-like-intelligent AI technology, organizations and developers are admiring its wonders in their digital work procedures.
DeepMind – an AI company- is trying to bring advancements to the IT industry through distinct programs and quick-witted technical concepts.
DeepMind’s Gato is a deep neural network-based multi-modal AI system built to execute more than 600 separate tasks efficiently. It has impressed the IT industry with its machine learning abilities as a multi-modal, multi-tasker that can play, chat and do numerous other things. However, with its mind-blowing ML expertise and smart features, Gato is giving tough competition to OpenAI’s GPT-3.
You can understand the power of learning that DeepMind’s Gato possesses, as it was challenged to learn games by itself and learned the game tactics without modifications to the code.
Moreover, Gato has also been challenged with a board game as it challenges AI due to its complex nature in deciding on the right in-game decisions.
DeepMind built a project called AlphaGo using supervised learning AI models, and it became the top worldwide player in 2017. DeepMind was also used to develop Google Assistant and helped create customized apps in Google PlayStore.
OpenAI GPT-3 versus DeepMind Gato:
Undoubtedly, the AI powerhouses, OpenAI vs. DeepMind, are trending on the internet, and GPT-3 and Gato are prime examples. Nevertheless, it might take some time to take a path from deep learning to artificial general intelligence.
Regarding OpenAI vs. DeepMind, Gato and GPT-3 are efficient and perfect examples of artificial intelligence systems. However, unluckily these systems were incapable of achieving AGI. But both companies are striving to achieve and implement AGI in their systems.
Moreover, both multi-modal require strong filters that could help them eliminate the flaws, faults, and errors that these robust systems still encompass. To eliminate these flaws, AGI can help these systems produce flawless and verified results.
Gato and GPT-3 are capable of achieving better results and performing seamlessly in the consumer market. However, AI experts still want to see these models achieve general AI.
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What about ethics when it comes to AI?
To obtain error-free results, GPT-3 and Gato need strict filters that eliminate errors and drawbacks like bias, racism, and offensive content.
We can get such results if both companies could implement general AI to Gato and GPT-3 to enable these intelligent architectures to adopt human abilities such as understanding, learning, and performing intellectual activities independently.
AGI’s cognitive computing capabilities enable it to study the decision-making and behavior of the human mind and resolve complexities.
However, when it comes to implementing ethics in AI-powered products, AGI could respond positively by addressing key challenges that IT firms are dealing with in their systems. Including difficulties in learning human-centric abilities like sensory perception, motor skills, self, and efficient problem-solving skills, human-level creativity, and more.
So, Will we ever achieve the expected AI revolution?
Most AI-based programs or software perform tasks that reflect the highest levels of human intelligence. Such software can perform pre-defined tasks but cannot perform any other task that’s not being taught, which stops them from understanding and learning at the advanced levels.
However, numerous surveys show that almost half of AI experts think it’s possible that AGI can be achieved, but it would take longer. But the ongoing swift advancement in the tech world shows that AGI will happen sooner.
But the question is, will self-decision-making AI-based robots or software likely be evil or bliss to the entire world? Also, don’t forget that AI has made a significant contribution in our lives (both personal and professional) that today’s humans and, eventually, industries need more cutting-edge and automated solutions to proceed seamlessly with daily tasks.
Also, advancements in technologies like artificial intelligence and machine learning have their own drawbacks and leveraging aspects that might include AI-produced bias, racism, and other misuses because, after all, AI is a technology that’s still in the developmental phase.
Though AI has opened new doors that lead us to new roads to innovations in industrial and business approaches, AI experts and researchers are still striving to address the problems in the best possible way to automate seamless processes.
Returning to the point that advancements in AGI will impact the world in more than two ways. Machines would be dominant over humans, thousands of jobs would be replaced with AI advancements, or humans will eventually get used to working alongside general AI-powered machines/software.
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Tech-savvies will keep leveraging AGI in multi-modal systems that might properly distinguish between OpenAI and DeepMind innovations. AGI technology can make more powerful and self-decision-making systems that would even be able to solve a range of problems on their own.
Looking at AGI from an organizational (OpenAI and DeepMind) point of view, they are sheer-focused on integrating AI into their systems, like GPT-3 and Gato. However, implementing AGI in these systems is costly and requires expert AI collaboration to make it happen.
In addition, identifying a significant path to progress within the AI field requires a lot of effort, effective long-term plans for the pros and cons of general AI, and partnerships across other industrial domains. This way, the scientific approach to researching artificial intelligence’s impact on society could help bring effective AGI solutions to our systems, but will AGI really be a blessing in disguise?
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