It's well documented that AI is a power-hungry technology. But there has been far less reporting on the extent of that hunger, how much its appetite is set to grow in the coming years, where that power will come from, and who will pay for it. For the past six months, MIT Technology Review's team of reporters and editors have worked to answer those questions. The result is an unprecedented look at the state of AI's energy and resource usage, where it is now, where it is headed in the years to come, and why we have to get it right. At the centerpiece of this package is an entirely novel line of reporting into the demands of inference--the way human beings interact with AI when we make text queries or ask AI to come up with new images or create videos.

The cost of making further progress in artificial intelligence is becoming as startling as a hallucination by ChatGPT. Demand for the graphics chips known as GPUs needed for large-scale AI training has driven prices of the crucial components through the roof. OpenAI has said that training the algorithm that now powers ChatGPT cost the firm over 100 million. The race to compete in AI also means that data centers are now consuming worrying amounts of energy. The AI gold rush has a few startups hatching bold plans to create new computational shovels to sell.

In AI development, the dominant paradigm is that the more training data, the better. OpenAI's GPT-2 model had a data set consisting of 40 gigabytes of text. GPT-3, which ChatGPT is based on, was trained on 570 GB of data. OpenAI has not shared how big the data set for its latest model, GPT-4, is. But that hunger for larger models is now coming back to bite the company. In the past few weeks, several Western data protection authorities have started investigations into how OpenAI collects and processes the data powering ChatGPT.

Today's date is April 13th, 2023. Since the public unveiling of ChatGPT couple of months ago, a large language model developed by OpenAI, we have been experiencing a continuous, probably exponential, advancement in AI which could cause the world, as we know it today, to be completely different in just a couple of years. One of the latest ground breaking experiments involved enabling GPT Agents to speak with each other, with a human merely specifying the context of the conversation before hand, e.g. "find a potential cure for cancer" and the two agents would start researching and dig deeper and browse the internet, perform researches, run simulations and do calculations that would be unfeasible for most researchers to do. If we'd compare AI to a human, then it would be only a couple of weeks old.

Ending global hunger has long been a critical goal for the global community. When the United Nations' Sustainable Development Goals were released in 2014, ending hunger, food insecurity and all forms of malnutrition formed SDG2. Though there has been some progress in the fight against hunger – ongoing conflicts, climate change, economic downturns and the COVID-19 pandemic have been major barriers to achieving SDG2. As of 2020, according to the UN, 720 and 811 million people globally faced hunger, and current estimates suggest that 660 million people may still face hunger in 2030. Professor Salah Sukkarieh, a robotics engineer at the University of Sydney's Australian Centre for Field Robotics, will this week speak at the United Nations Food and Agriculture Organization's (FAO) Global Conference on Sustainable Plant Production in Rome (2-4 November).

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Are giant AI language models like GPT-3 or PaLM under-trained? A Deepmind study shows that we can expect further leaps in performance. Big language models like OpenAI's GPT-3, Deepmind's Gopher, or most recently Google's powerful PaLM rely on lots of data and gigantic neural networks with hundreds of billions of parameters. PaLM, with 540 billion parameters, is the largest language model to date. The trend toward more and more parameters stems from the previous finding that the capabilities of large AI models scale with their size.

It's hard to believe it now, but just four short years ago articles on quantum computing were a bit of a dud when it came to catching the attention of mainstream news consumers. Oh what a difference a few years can make. You can't open the science section on Google or Apple News nowadays without seeing a near-equal split between space and quantum physics stories. The world's fallen in love with speculative science again thanks to quantum computing and artificial intelligence, and I couldn't be happier. Attend the tech festival of the year and get your super early bird ticket now! I believe quantum computing has the ability to impact our species in a way that nothing short of the emergence of a physical God or the arrival of an advanced alien benefactor can.

Research has found that using nanotechnology and artificial intelligence (AI) in agriculture could offer a practical solution to the challenges threatening global food security. The study, carried out by researchers at the UK's University of Birmingham, investigates how'precision agriculture' would allow farmers to respond in real time to changes in crop growth using technology. 'Precision agriculture' refers to farming methods which measure and respond to variability in crops, allowing management of land with the goal of optimising efficiency and reducing waste. In this case, AI and nanotechnology could be used to help both crops and soil perform better. Climate change, increasing populations, competing demands on land for production of biofuels and declining soil quality have all made it more and more difficult to feed global citizens.

The global pandemic has given us all an opportunity to pause for thought and take stock of what is and what is not important. More and more businesses are turning to AI to become more sustainable, smarter and to better react to changing market conditions, as well as to ensure health, safety and social impact of our planet. We need a future where you can do the things you love; live the life you deserve and take the time to grow with nature and nurture the things that inspire you to help others. From pandemic prevention and fighting cancer, to fighting hunger, wildlife conservation and boosting accessibility, this article will explore exactly how AI is doing well by doing good. AI use cases can help towards overall adaptation in preventing wildfires, diagnosing deadly diseases, mitigating risks posed in critical areas as well as predictive analysis and monitoring to make our planet more resilient in the near future.