Without any doubt, we are living in the golden age of artificial intelligence. Every day, artificial intelligence is becoming increasingly accessible and is profoundly transforming various sectors including business, health, entertainment, and more. From Elon Musk's Neuralink making strides toward chip implantation that improves cognitive function, to Google's AutoML making AI accessible for businesses of all sizes, the landscape of AI is vast and diverse.

Let's start with something fresh, NVIDIA's recently demonstrated GauGAN2. It's an AI tool enhancing image generation technologies, making it possible for anyone, even without artistic skills, to generate realistic images just from a basic sketch. This might seem trivial, but consider the potential impact in sectors like game design, advertising, or architecture where life-like visualization can save cost, time, and stimulate innovation.

On a more fundamental level, the development of more potent AI models such as OpenAI's GPT-3 should be noted. GPT-3 stands out for its impressive language understanding and generation capabilities, but it’s not an end in and of itself. It's part of a generational upgrade in AI, and its successors are expected to be even more capable. GPT-3 has been utilized in myriad applications from copy-generation, customer support, drafting emails, and even writing code. But imagine the next iterations – we could see AI that writes books, generates creative scripts, or even aids scientific discovery through a more profound comprehension of text.

Another current focus in the AI ecosystem is the pursuit of Fairness, Accountability, and Transparency in Machine Learning (FAT ML). As AI revolutionizes everything from retail to diagnostics, it is crucial that the technology accurately represents and benefits all users. Companies like IBM and Google are investing in understanding AI-bias and creating fair AI systems. For instance, IBM's AI Fairness 360 is an extensible open-source toolkit providing metrics to check for unwanted bias in datasets and machine learning models.

On the hardware front, companies like Google are developing purpose-built ASIC chips such as Tensor Processing Units (TPUs) that provide significantly enhanced computational power to meet the ever-increasing demands of AI models without skyrocketing energy costs. This pushes the boundaries of possibility in large-scale machine learning operations.

Finally, there is a strong push towards AI that can learn from less data, or even single examples – one-shot learning. This is where DeepMind's recent AlphaFold makes its mark, predicting protein structures with accuracy, previously considered impossible. It has implications for a greater understanding of life, disease, and drug development.

In conclusion, AI is an ever-evolving field, with new developments emerging every day changing how we work, live, and communicate. Despite the challenges facing AI, such as ethics, privacy, and data security, the potential for positive change is immense. Witnessing these shifts in real-time is a testament to the extraordinary era of innovation we live in. Watch this space for more exciting daily updates in AI.