Trying to Predict the Future

There have been huge advances since old fashioned symbolic AI. I am in my 70s and I have worked in various fields of AI since 1982 so I ask you, dear reader, to take my word for this.

Symbolic AI was based on the idea that intelligence could be achieved by manipulating symbols and rules. However, this approach had its limitations and was eventually surpassed by newer techniques.

One of these techniques is neural models. Neural models are based on the structure and function of the brain. They use interconnected nodes to process information and learn from data. Classic machine learning is another technique that has been developed. It involves using algorithms to learn from data and make predictions or decisions. I served on a DARPA neural network tools committee in the 1980s when I also wrote the commercial Windows-based product ANSim neural network library and created a back-propagation model for a bomb detector my company built under FAA contract.

Deep learning is a more advanced form of machine learning that uses neural networks with many layers. These layers can extract increasingly complex features from the data. LLMs (Large Language Models) are a recent development in AI that use deep learning to generate human-like text. The future of AI includes further advancements in these techniques and the development of new ones.

As I write this book, dear reader, I have been using Transformer models for about four years and LLMs for two years. We are just scratching the surface developing new technologies and products using LLMs. My predictions for the near future represent two different paths:

• There will be a large proliferation of smaller LLMs that are focused on specific tasks like coding assistants, text generators and writing tools, integrative tools to enable robots to plan and follow instructions taking advantage of the real world knowledge contained in LLMs, etc.
• Large models like OpenAI’s GPT-4, Facebook’s LLaMA models, Google’s PaLM 2 models, etc. will continue to evolve through both computational and hardware advances that greatly increase efficiency, as well as encode more real world knowledge and knowledge of human languages.

The future of AI for small devices and edge computing is very promising. Edge AI is a new frontier that combines edge computing and AI. It allows for faster computing and insights, better data security, and efficient control over continuous operation. As a result, it can enhance the performance of AI-enabled applications and keep operating costs down.

Edge AI facilitates machine learning, autonomous application of deep learning models, and advanced algorithms on IoT devices itself, away from cloud services. This can benefit various industries, from improving production monitoring of an assembly line to driving autonomous vehicles.

The recent rolling out of 5G technology in many countries gives an extra boost for Edge AI as more industrial applications for the technology continue to emerge. The future advantages of combining Edge Computing and Edge AI are significant.

Apple and Google have built custom silicon for running neural models on small devices.

This is just my opinion, but I think that Google devices get a high score for security and Apple devices get a high score for privacy. As consumers we can all hope for continued improvements in security and privacy from manufacturers like Apple, Google, and Samsung. For phone brands like Huawei, Xiaomi, OnePlus, and OPPO that are made in China the question of privacy is complicated by Chinese laws giving the government access to consumer data. In some ways the situation is similarly unclear and troubling in the USA: it is possible that the largest purchasers of data on US citizens are government intelligence and police services. Troubling indeed!

Personalized AIs are becoming increasingly popular as technology advances. These AIs are designed to cater to the specific needs and preferences of individual users, providing a more tailored and user-friendly experience.

One important aspect of personalized AIs is data security. It is crucial that all user data remains on their devices unless it is encrypted for archival to the cloud. This ensures that user data is protected and secure.

Another important aspect of personalized AIs, and a general topic in this book, is the use of open and public LLMs (Language Learning Models) running on user devices. These models should be trained from open and publicly scrutinized datasets, ensuring transparency and accountability in the use of AI technology.

In addition, all AI software that interacts with user data and assists in daily tasks should be open source and audited. This allows users to have greater control over the AI technology that they use, and ensures that it is transparent and trustworthy.

While these ideas may not be widely shared, they represent a personal wish list for the ideal implementation of personalized AIs. By prioritizing data security, openness, and transparency, we can create a more trustworthy and user-friendly AI experience.

Personalized AIs have the potential to revolutionize the way we interact with technology. By prioritizing data security, openness, and transparency, we can create a more trustworthy and user-friendly AI experience for all.

We are all free to make our own life decisions. My hope is that in the future we will have options that allow those of us who desire it to shift towards using non-intrusive digital devices that integrate seamlessly into our lives without constantly pulling at our attention. This trend could be the beginning of a digital revolution where technology is seen less as a distraction and more as an empowering tool. As an AI practitioner I expect non-intrusive AI to play a major role.

Imagine a world where digital devices provide you with what you need, exactly when you need it, and then quietly fade into the background. This is the promise of ambient computing, a concept that envisions a world where technology becomes so well integrated into our lives that it disappears. Ambient computing is about smart devices anticipating your needs, offering solutions before you’re even aware you have a problem. For instance, an AI-powered refrigerator could note your depleted grocery stocks, prepare a list, and order them for you, eliminating the need for you to manually check and place an order.

It’s worth noting that this new paradigm doesn’t mean that we use less digital technology, but rather a shift in how it is consumed. The aim is to remove the intrusive aspects of technology—those elements that incessantly demand our attention—and replace them with non-disruptive processes.

There is ample research that shows the harm of short attention span consumption of digital media. I believe that platforms like TikTok, YouTube Shorts, Facebook, Instagram, etc. are not good for our brains physically, our contentment and satisfaction with life, and our mental hygiene.

One of the key advancements driving this change is the ongoing development of machine learning algorithms and AI. By analyzing our digital footprint - our likes, dislikes, habits, schedules - these technologies can become effective personal assistants. They can intuitively understand what we want, without us having to stop, pull out a phone, and start typing or speaking commands. To safely design such systems there are two priorities:

• Privacy: all models and data should stay on our devices and under our control.
• I think the key to designing human-first AI systems is training them to not interrupt us frequently, rather favoring presenting us with information for long form consumption.

This move towards non-intrusive digital devices also hints at an era of wearable technology. Imagine having a digital assistant housed within a pair of eyeglasses or even a contact lens. Such a device could provide you with timely and contextual information, without requiring you to look at a screen. Similarly, advanced health trackers could monitor your vital signs and provide you with timely medical advice without any proactive effort on your part.

Moreover, the future might also involve the expansion of digital minimalism, a philosophy that encourages conscious, purposeful use of technology. Instead of mindlessly scrolling through social media feeds, the non-intrusive devices of the future will promote meaningful engagement with the digital world. They will allow us to stay connected and informed without overwhelming us, aligning technology more harmoniously with our natural rhythms of attention and rest.

So dear reader, my hope is a future of non-intrusive digital devices with accompanying AI capabilities, a world in which technology is seamlessly integrated into our lives. It’s a future where devices respect our attention, time, and privacy, enhancing our lives without causing unnecessary disruptions. We stand on the brink of a new era, where digital technology will serve us, not the other way around. The age of digital serenity is, perhaps, closer than we think for those of us who desire it.

Today, devices like the Apple Watch provide some of the features I want:

• Siri interactions for the most part don’t use cloud services and are mostly done on-device.
• The ability to send and receive texts and emails and make phone calls is often sufficient so we can leave our phones at home.
• Combined with AirPods, the watch is sufficient for long-form entertainment like podcasts, audio books, and music.
• The Apple Watch works poorly for consuming short-attention span social media; I think this is a good thing!

While I am currently using Apple gear, there are also very good Android devices to consider. There are several Android smartwatches that offer features similar to the Apple Watch. Some of the smartwatches for Android have built-in voice assistants like Google Assistant, the ability to send and receive texts and emails, make phone calls, and play music include the Google Pixel Watch, Samsung Galaxy Watch 5, etc.

I think the important decision is to commit to using devices, AI platforms, and infrastructure that lets us enjoy a natural human life while still providing entertainment and AI assistance when we need it. For me, dear reader, smartphones don’t meet these requirements.

I predict that non-intrusive AI-powered devices will continue to evolve with more available product options.