We have noted that AI is the topic of the day in tech circles recently, but it has been the focus of the private investment community for a much longer time.  Global AI start-up funding has increased from ~$26.6b in 2019 to $52.1b last year, only 20% less that the value of the entire AI market in 2020.    Along with VC funding excitement, which continues currently, a number of AI companies have gone public, some at unusually high valuations.  We were curious to see how those valuations have been maintained as the AI craze continues and were surprised to see that they have not held up as well as one might have thought given the level of publicity.

In the table below, we look at a number of medium to large AI related IPOs and how the valuations have changed.  This is not a complete list, as there have been a number of smaller AI related company IPOs, and we have excluded all foreign AI company IPOs.  We show the latest quarter sales and operating income, and the latest full year sales and operating income, and a brief description of the company.

​Columns 3 and 4 tell the whole story, and with the two acquired companies the valuation slides from ~$57b to ~$14b, a decline of ~75%, although many of these deals were done near a general tech peak in 2021.  As these are typically relatively young companies, they might one day, be able to grow into those lofty AI valuations, but we expect many AI VCs have realized that they were able to cash out of at least some of their AI investments before the cycle began to turn and have been a bit more conservative about valuations since.

Are you worried that Ai might replace you?  Are you thinking about a career change because of AI?  Did you decide to skip taking that English course at the New School because of AI?  These all  ight seem far-fetched concepts but there are those who have already felt the touch of AI, and its not a kind one.  While robotics were supposed to put a major portion of the population out of work years ago, the workplace evolved over time and robotics has taken a much longer time to find its was into the workplace than originally thought.  That said, AI has far fewer mechanical and physical limitations that robotics, which implies it will entangle itself in human society more quickly.  AI has tremendous power, albeit based on human algorithms, but ene in its present early state, it can do lots of things more quickly and efficiently than humans.
We note that two advertising companies in South Korea have both decided to completely stop outsourcing creative design, proposal writing, copywriting, and all other freelance work, in order to use AI related tools that can accomplish those same tasks less expensively and more quickly, under the guidance of its own employees.  A large noodle company (unknown which) has also decided to give up its outsourced projects, at least at the draft level, as it found an Ai system was able to give it more options for illustrations in less time by using keywords than outsourcers.  The company estimates they can save ~2% by just using Ai at this relatively basic level.
At this stage, unless the Ai is highly trained to a specific dataset, results are good, but lack the creativity of a human, at least a human who has some level of creativity, but there are many ‘draft’ jobs in media that do not require creativity, and many of those have already been given over to bots that take digital news feeds and work them into short headlines and copy that are part of global news feeds.  They are far from creative and anyone familiar with the process can usually spot them quickly, but they will get better over time as will AI illustrators and similar media-oriented content production, and they can work 24/7 without coffee or conversations in the break room.  It would seem that the run-of-the-mill copywriter, illustrator, or video clip editor is going to get a run for their money by AI in the near-term, as editors can request an Ai to give 2 or 3 versions of everything it writes, allowing the editor to choose the best, until one day the division manager replaces the editor with an Ai that chooses which is best, and so on up the chain.  It seems wonderful to think that someday we can all relax and sit in our backyards letting a massive AI infrastructure do all the work for us that is until we lose that creative spark that created AI in the first place.

Earlier this week we noted that while OpenAI’s (pvt) ChatGPT is a general-purpose Natural Language Model (NLM), adding front-end code that references specific datasets can create systems that can be tailored to more specialized applications, opening up NLMs to the real world of data analysis.  These applications can be specific to an industry, a product type, or customer information, but they all share one common issue and that is they must be reliable.

ChatGPT and Google’s (GOOG) Bard are subject to inconsistencies in their responses ranging from incorrect information, incorrect conclusions, and referencing information that does not exist.  Data scientists are not sure why large NLMs have this issue, but we have faced such situations even in our limited use of NLMs.  ChatGPT, given that its training ended in late 2021 is careful to point out that its data is historically limited to the date on which its training ended and might not have more current information, despite its insistence that it has been updated a number of times since then. Google’s Bard however indicates that it is constantly being updated and states that ‘My training is never finished’, leading one to believe that its data is up to date, yet we find inconsistencies in Bard’s responses that indicate a lack of self-checking that is essential for use in most business circumstances.

Here was our simple test:

 Q: How many VR headsets were sold in 2022?

A: According to the International Data Corporation (IDC), global shipments of AR/VR headsets declined 20.9% year over year to 8.8 million units in 2022.

Q: Can you break that down by quarter?

A: Sure. Here is a breakdown of global shipments of AR/VR headsets by quarter in 2022:
Quarter                           Shipments (in Millions)
1Q                                   1.6
2Q                                   2.3
3Q                                   2.7
4Q                                   2.2
 
Q: Can you break that down by brand?
 
A: Sure. Here is a breakdown of global AR/VR headsets by brand in 2022.
 
Brand                              Shipments (in Millions)
Meta                               4.6
HTC (2498.TT(                 2.1
Sony                                1.1
Others                             1.0
 
Q: Can you break down the full year by quarter for each brand?
 
A: Sure.  Here is a breakdown of global shipments of AR/VR headsets by brand and quarter in 2022.
 
Brand                              1Q         2Q         3Q         4Q
Meta                               1.4        1.8        1.7        1.7
HTC                                 0.6        1.0        0.5        0.1
Sony                                0.3        0.3        0.3        0.2
Others                             0.3        0.2        0.2        0.1
 
If we add the quarters in last set, the full year comes to 10.7m units and each brand’s yearly total is different from the full-year totals given in the previous questions.  We asked Bard the following question to clarify:
 
Q: Why do the quarterly VR brand numbers differ from the yearly brand numbers?
 
A: There are a few reasons why the quarterly VR brand numbers differ from the yearly brand numbers.
One reason is that the VR market is still in its early stages of development, and there is a lot of volatility in the market. This means that the quarterly numbers can be affected by a number of factors, such as the release of new headsets, changes in the market, and economic conditions.

Another reason is that the quarterly numbers are based on shipments, while the yearly numbers are based on sales. This means that the quarterly numbers can be affected by factors such as inventory levels and the timing of shipments.

Finally, the quarterly numbers are often based on estimates, while the yearly numbers are based on actual sales data. This means that the quarterly numbers can be more volatile than the yearly numbers.
Despite these factors, the quarterly numbers can still be a useful tool for tracking the VR market. By tracking the quarterly numbers, you can get a better understanding of the overall trends in the market and the performance of individual brands.
​
Here are some additional factors that can affect the quarterly VR brand numbers:

• Seasonality: The VR market is cyclical, with sales typically peaking in the holiday season. This can lead to higher quarterly numbers in the fourth quarter of the year.
• Marketing campaigns: VR brands often launch marketing campaigns in the run-up to major events, such as the Consumer Electronics Show (CES). This can lead to higher quarterly numbers in the quarter leading up to the event.
• New product releases: VR brands often release new products in the first quarter of the year. This can lead to higher quarterly numbers in the first quarter of the year.
• Economic conditions: The VR market is sensitive to economic conditions. When the economy is doing well, people are more likely to spend money on discretionary items like VR headsets. This can lead to higher quarterly numbers in economic boom times.

Overall, the quarterly VR brand numbers can be a useful tool for tracking the VR market. However, it is important to keep in mind the factors that can affect the numbers and to use them in conjunction with other data sources to get a complete picture of the market.

If we had not asked the clarification question or fact checked the answers, we could have based a decision on information that was not correct. The system’s initial reasoning for the differences in its stated results (market volatility) was suspect as it had no bearing on why simple math produced different results from facts that the system presented originally.  The 2nd explanation (some were shipments, some were sales) was inconsistent in that it was not specified in the initial answer, an absolute necessity, in both the actual questions and the system’s specific answers, which were always said to be shipments by the system for VR   Only when questioned did the system indicate that some of the numbers were referencing sales rather than shipments. 

Additionally the system seems to use an ‘answering’ technique often employed by politicians when asked questions they do not want to answer or cannot answer, that of adding a hefty supply of information that does not directly relate to the answer, hoping that the additional verbiage will mask the fact that the answer was not given  The scary part of that theory is that such a technique is a very human trait that had to be programmed into the system or ‘learned’ by the system after seeing the practice used in its training data.  Maybe we are anthropomorphizing Bard a bit too much, but picking up bad human characteristics is not something we hope for or expect from AI.

Regardless, no matter what the circumstance, a NLM is only as good as its training and inference algorithms and its training data, which leads us back to the point that no matter how large the data sets or how specific they are to the required answers, the accuracy, precision, and recall parameters (also known as the ‘F1’) is the key to understanding the reliability of the system.  When humans make decisions based on information presented, they also have an F1, with successful humans having a high F1, while those with low F1s are less successful when it comes to decision making.  As each human programmer has an F1, the accumulated F1’s of the programmed algorithms would be an integral part of the system’s F1, so we can’t blame the system for coming up with incorrect answers.  Perhaps a year backpacking across Europe might help Bard to gain some needed perspective and get down to finding its own personality and consistency.

​OpenAI’s (pvt) ChatGPT chatbot has its limitations.  Among the most important to users is a time constraint.  Not one that prevents you from using the model for extended periods of time, but the fact that the system’s learning period ended in September 2021, which means it is unaware of things that have happened since then and cannot integrate them into its responses.  ChatGPT was also trained on specific data, much of which came from the internet, so the quality of that data can and does influence ChatGPT’s responses, which it couches with “I can sometimes provide incorrect or biased information.”  That said, if one knows the ins and outs of ChatGPT, as we have noted previously, the system can be extremely helpful and unusually prescient.
The problem is however that newer learning models, and when we say newer, we mean the data they were or are being taught on, is more up to date, inclusive of current events, and data from 2022, which pushes one to try other NLMs.  The folks at Open AI are not taking this competitive issue sitting down (actually they are probably are sitting down), but as of today they are making available a plug-in version of ChatGPT that developers can use to give ChatGPT access to up-to-date information, or specific data that 3rd party developers can provide.  If you are willing to join ChatGPT+, for a mere $20/month, you now have access to over 70 3rd party plug-ins, available at the ChatGPT plug-in store, that allow ChatGPT to perform a wide variety of functions based on.
Some examples might be sports scores (“ChatGPT, who was the player with the most goals in the last 10 minutes of the 3rd period during the last 7 games of the 2021 season?”), stock information (“ChatGPT, How many times did GLW violate its 30 day Moving Average during each Friday in 2019?”), or even more simple tasks like ordering food (“ChatGPT, How many restaurants within 10  miles with ratings above 4 serve Nabe as part of their take-out menu?”).  As the plug-ins are developed outside of ChatGPT, the data access is provided by the plug-in but the ChatGPT data and services, including an OpenAI verification process  remains accessible, particularly the language processing that makes ChatGPT conversational. 
Open AI provides a variety of examples, including code.  Here are a few:

• Keywords – Extract keywords from a block of text
• Ad from Product Description – Create ad copy from a product description
• Tweet Classifier – Decides whether a Tweet’s sentiment is positive, neutral or negative.
• English to Other Languages – Translates English into French, Spanish, or Japanese.
• Product Name Generator – Creates product names from example words.
• Marv the Sarcastic chat bot – Marv is a factual chat bot that is also sarcastic.
• Create Study Notes – Creates study notes from a given topic.

A few actual ChatGPT Plugins that are already available:

• KalendarAI – KalendarAI sales agents generate revenue with potential customers from 200+ million companies globally.
• Cloudflare Radar – Get real-time insights into internet traffic patterns and threats as seen by Cloudflare.
• Polarr – Search Polarr’s massive pool of user generated filters to make your photos and videos perfect.
• FiscalNote – Provides and enables access to select market, trading, real-time datasets for legal, political, and regulatory data and information.

When using ChatGPT with a plug-in it opens ChatGPT to additional databases that were not available to the NLM previously, giving ChatGPT new or highly specific information that it did not learn during its training phase in 2021, such as summarizing a personal or business document and translating it into a different language, or recommending a ‘quiet hotel in Paris that allows pets”, and then books the car to the airport, the flight, the car to the hotel, the hotel reservations, and the returns once you approve.  But the idea of using 3rd parties to create plug-ins for ChatGPT will open the NLM to data that it would never have access to, such as stock market trading data or SEC filings, which increases its usefulness by many orders of magnitude, and will lift Ai from being a curiosity, like Pong was in 1972 to Minecraft level (200m+ copies sold).

​As we have noted previously, AI systems are not just rows of processor cards with blinking LEDs but are systems that must be almost spoon-fed information upon which algorithms provided to them, make decisions when queried.  Before such systems become viable, they need to be taught, using vast amounts of data and ‘teaching’ algorithms that are provided by data engineers and are separate from processing algorithms.  That said, before the systems are taught, the data must be ‘organized’ into data sets that the system can recognize, which must be accomplished by humans sitting in front of computers.  Each of these processes are different, involving both humans and computers and computers alone, but they all have one thing in common and that is that they all use energy.
While the current AI focus has put such systems in the public eye, but computers have been sucking down power for years, and while models can make estimates of what percentage of the global power grid they use and how much it increases each year, there is no specific monitoring, or is there a way to come up with an accurate estimate.  The models estimated that in 2018 ~1% to 2% of the global power supply was used by computers, with 2020 as high as 4% to 6%, and models predict that at that rate it will reach anywhere from 8% to 20% by 20230, and most of that modeling was done before the recent AI fanfare and funding.
Carbon emissions are certainly a concern as demand increases but there is also a longer-term concern that with that kind of consumption growth, capacity will not meet demand.  AI systems will get more efficient and consumption/cpu will decline, but if CPU volume growth continues or accelerates, as it seems to be current doing, who will get ‘power priority’, the average TV viewer or the AI system that is running the local telecommunication system?  Of course, this is a bit sensational, although still a concern when trying to balance a green environment and continued demand for energy, but Microsoft (MSFT) seems to be thinking ahead a has signed a deal to ‘guarantee’ its potential energy needs in the future.
According to reports, Helion (pvt) a start-up funded by Sam Altman, the current CEO of OpenAI (pvt), the company that developed ChatGPT, Dustin Moskowitz, a founding member of Meta (FB), and Reid Hoffman, the Co-founder of Linkedin (MSFT), has agreed to provide Microsoft with power it generates by 2028.  While this might not seem unusual, Microsoft likely has an army of negotiators working with utilities across the globe, this source is a bit different as Helion’s technology is based on nuclear fusion.  Helion is so positive that they can accomplish this goal that they have agreed to generate at least 50 Megawatts in the following year or pay a penalty, all of this with the understanding that to date fusion technology has not produced any electricity.    Helion has even signed an agreement with Constellation Energy (CEG), the owner of the largest nuclear power plant in the US, to manage the Helion project and market its output.
Helion has promised to demonstrate a working prototype next year, with Mr. Altman adding, “Our goal is to make the coolest tech demo in the world.  Our goal is to power the world and do it extremely cheaply.”  Of course such optimism comes after one pumps $375m into Helion but he insists Helion will be able to deliver power to the global grid by 2028 and checks in on the company roughly once a month.  The fusion industry did receive a boost last December when scientists at Lawrence Livermore National Laboratory were able to generate more energy that they put in during a fusion reaction, but on a net basis that is a far cry from being a commercial fusion producer at a cost that is competitive with other energy modalities.  Is Microsoft drinking Altman’s very expensive Kool-Aid after signing a multi-billion-dollar partnership with Altman’s OpenAI, or do they know something no one else does?  We should know by the end of next year.

As artificial intelligence continues to appear in the headlines, both positive and negative, there is one group that is certainly benefiting from the publicity.  Companies, Associations, and universities are hiring lobbyists to make their views known to the federal government.  Over 120 public and private entities lobbied the government in the 1st quarter of this year, with 158 doing the same for all of last year and 30 in 2017.  The more obvious companies that have a major stake in the technology, such as Amazon (AMZN), IBM (IBM), Microsoft (MSFT), and Nvidia (NVDA), were joined by the American Bar Association, the Consumer Technology Association, the Recording Industry Association, and the Screen Actors Guild/AFTRA, along with Carnegie Mellon University, Case Western Reserve University, Harvard University, Southern Methodist University, and Washington State University, in making their opinions known to the government, and, of course, a number of pure AI companies themselves.
 
A number of large companies suddenly realized that they were behind in the ‘AI race’ and have announced plans and projects to find ways to integrate cognitive AI into their platforms, with Microsoft leading the way with plans to integrate ChatGPT into Bing search.  A number of these companies cut Ai project staffing at the end of last year as the technology space  contracted, especially those Ai projects relating to Ai ethics, are now planning to build Ai project staffing to maintain an active level of competition with their peers., while the US Chamber of Commerce, the largest lobbying organization in the US, spent ~$19m in 1Q, including establishing a taskforce on AI in the House Committee on Financial Services, implementing the National Artificial Intelligence Act of 2020 (primarily ‘studies’ and standards research), and began drafting bills relating to automated vehicles.  At least three major insurance companies lobbied for congressional efforts to better understand the commercial use of AI, and a number of universities lobbied to support the Army AI Center and distributed AI applications for defense, as they likely get funded as part of the center’s project budget.
 
​

​As we noted previously, there has been a a bit of backlash from a large group of researchers and academics concerning the rapid pace at which AI systems are being developed, with an emphasis on the ethical use of such software, with some of the more extreme groups pushing for an investigation into OpenAI, the developer of ChatGPT, citing GPT-4 as ‘biased, deceptive, and a risk to privacy and public safety”, while lobbying for ‘algorithmic transparency’, as comments by IBM that they would be replacing 7,800 workers with Ai systems over the next few years sparked news organizations to create headlines like: “AI could replace equivalent of 300 million jobs – report” (BBC – 3/28/23), ChatGPT: the 10 jobs Most at Risk of Being Replaced by AI (business Insider- 4/9/23), or “How AI is Replacing Jobs 2023: A Look at the Statistics (Gitnux Blog – 3/23/23) that cited “AI is estimated to replace 85m jobs by 2025, but 97m new jobs will be created due to AI”, sort of a bad news/good news headline that would likely incite almost any labor-related organization, and send publicity seeking Congress men and women into a media frenzy.
It is obviously quite important that the government understands the Ai industry and the impact it can and will have on society and US economics, but we fear it will become both the media calling card and a path to screen time for politicians, and then fade away when a new hot button issue takes its place, just like the Metaverse did last year.  AI, particularly the ethics and legalities of AI are extremely complex issues and the odds are that politicians are not going to spend a lot of time on them unless it affects them directly, perhaps when it affects a large campaign donor.  Lobbyists are trying to push their client’s agendas to politicians now that the public has been made aware that they now have another ‘fear’ to consider, without the positive balance that shows what AI is able to do for society.  But eye-catching headlines are not usually made from “AI Could Help to Cure Cancer” over “AI Will Take Your Job Soon”.  At best, we can expect the government to form more committees to study the problem when we could use AI to end Gerrymandering by balancing population districts, polling constituents to find out how they feel about a variety of topics, or dig through local, state, and federal budgets to look for fraud, overspending, or projects that benefit only a small portion of the electorate, the sort of things politicians are supposed to do.  Perhaps the better headline would be, “Politicians to be Replaced by AI by 2028”.

Ai is a bit far afield from our usual hunting ground, consumer electronics, but recent stories in the press about ChatGPT have sparked interest from some of our readers toward the topic, and without even stretching a little, we can easily draw the increasing connection AI has to consumer electronics.   Most consumers’ perception of AI is a large bank of servers, lights flashing, churning over billions of data points in order to arrive at a conclusion, but that is only one part of the AI story, and usually the part that has little to do with consumer electronics.  But some form of AI is built into many consumer electronics products and performs numerous functions, some of which we never see or hear, while others are commonplace.
Among the most common “AI” tools is the spell checker usually included in word processors, an outgrowth of Ralph Gorin’s 1971 SPELL program that checked letters in a word against a list of correctly spelled words by comparing single or adjacent letters, and while early spell checkers were limited to Mainframes, they migrated quickly to PCs in the early 1980’s.  These relatively simple systems were based on a comparisons, which would imply that the more words to compare against the more accurate the checker, but it was found that as the word lists grew over 100,000 the number of misspellings increased. 
The problem facing early spell checkers is that they were not able to discern context, so they faced difficulties with words that sounded the same but had different spellings and different use cases, such as ‘there’ and ‘their’.  As local computing power and storage increased, systems were designed that could evaluate context by comparing phrases or sentences against grammatical rules that were embedded in the system and could determine the correct spelling of a word based on both the word list and the rules, but data scientists found that as processing power and storage increased further, they could enter millions of data points that AI systems could ‘look at’ to better understand context, not only for language, but for almost anything, leading to the development of AI algorithms that fall into three classes.
Supervised – The most common category, where labeled data is feed to a system and is used to train the system to predict outcomes as it is given new data.  Typical systems uses decision trees that test the data using sophisticated ‘if…then’ nodes to move through the decision process until a conclusion is drawn that is based on all of the tagged data that the system has seen.  Systems like these are the basis for systems that have been in the press in recent months, such as those that can ‘paint’ a picture based on the style of a particular painter, having been fed the digital images of all of that artists work.  The system can create an image based on the user’s request using the characteristics it has recognized for that artist.  In its simplest form we asked an AI art generator to paint a picture of daisies in a vase using a generic style and Figure 1 is the result.
Unsupervised – This category does not use tagged data, so the algorithm must evaluate the relationships between the data points without specific definitions, which they tend to do by creating clusters of similar data around central data points, and using a number of methodologies to evaluated the clustered data.
Reinforcement – This category is a bit like training an animal, as the system uses an ‘agent’ to perform an action within a certain ‘environment’, and then rewards the agent when the action is completed.  The agent uses the reward (positive or negative) to help it with the next iteration of the action until the environment ends the process, just as rewarding an animal for a correct response helps the animal to understand what the correct response is.

​CE products vary widely in their use of AI systems, from virtual assistants such as Amazon’s (AMZN) Alexa  or Apple’s (AAPL) Siri to embedded Ai systems such as those in your TVs.  Virtual assistants ‘learn’ about your behavior based on the tasks they are asked to perform, so, in theory, they should be able to respond to requests with increasing accuracy as they learn an individual’s patterns.  TV AI is more technical in that it can sample incoming images for patterns and make adjustments on-the-fly to emphasize or de-emphasize a particular aspects of the image based on the user’s settings, or remove ‘noise’ or scale an image from one resolution to another.  These systems can process images on a pixel-by-pixel basis and with TVs generating ~30 frames each second, that comes to 14.9b pixels that can be processed each minute, but the AI component also ‘learns’ about each image, rather than comparing to a built-in list as in older sets, and can make better adjustments based on what it learns.
Both of the applications mentioned are common to CE products and in many cases are integral to their function, but AI continues to develop and will become more integral in CE products and daily life.  Having a background in the music industry, we were intrigued by Google’s (GOOG) MusicLM system, a bit less sophisticated than the artistic systems mentioned above, but nonetheless amazing in its ability to create music from text.  In its simplest application the words “acoustic guitar” or “flute” generate the following sound clips[1]:
 https://google-research.github.io/seanet/musiclm/examples/audio_samples/10s_samples/instruments/acoustic-guitar.wav,
 https://google-research.github.io/seanet/musiclm/examples/audio_samples/10s_samples/instruments/flute.wav
but more interesting is how it generates clips from the text “blues”, “West coast Hip-hop”, “East coast Hip-hop”, or “Reggae”
 https://google-research.github.io/seanet/musiclm/examples/audio_samples/10s_samples/genres/blues.wav
https://google-research.github.io/seanet/musiclm/examples/audio_samples/10s_samples/genres/west-coast-hip-hop.wav
https://google-research.github.io/seanet/musiclm/examples/audio_samples/10s_samples/genres/east-coast-hip-hop.wav
https://google-research.github.io/seanet/musiclm/examples/audio_samples/10s_samples/genres/reggae.wav
However things get even better when the system gets more complex text that does not specify a genre but a caption, such as “beach in the Caribbeans” or “escaping prison”,
https://google-research.github.io/seanet/musiclm/examples/audio_samples/10s_samples/places/beach-in-the-caribbeans.wav
https://google-research.github.io/seanet/musiclm/examples/audio_samples/10s_samples/places/escaping-prison.wav
with applications in TV, gaming and advertising quite obvious.  Not only is the system able to create genre related music or even contextual music, but it has also learned about diversity across all forms of music and with the same text token “progressive rock guitar solo” came up with a number of different guitar solos based on its learned data.
https://google-research.github.io/seanet/musiclm/examples/audio_samples/diversity-samples/progressive-rock-guitar-solo/same-text/0.wav
https://google-research.github.io/seanet/musiclm/examples/audio_samples/diversity-samples/progressive-rock-guitar-solo/same-text/1.wav
https://google-research.github.io/seanet/musiclm/examples/audio_samples/diversity-samples/progressive-rock-guitar-solo/same-text/2.wav
 
 
While this is only a single AI application, there are many, including the famous/infamous ChatGPT and its many clones, that will find their way into CE applications regardless of whether we believe they are useful, helpful, legal, moral, or justified as they represent ways in which content can be created without the expenses associated with human involvement.  We have given up moralizing about the potential ‘disintegration of society’ that AI has the potential to cause, since almost all generations have heard the same thing about some technological change (TV, video games, CDs, etc.), and society has found a way to adapt to the change.  However, as only a small portion of humankind has the desire to challenge the validity of textual or visual media, it will take some time before we understand how AI will affect the fabric of human creativity.  Will it turn us into a society of eloi[2], allowing AI to keep us docile with a constant supply of curated entertainment, or will it free us from the burdens of everyday life, allowing our creativity to blossom?  Honestly, we don’t have a clue but to paraphrase Bette Davis “Fasten your seatbelts, it’s going to be a bumpy ride.”


[1] These sound clips reference the Google MusicLM site links for each file.  Ctl+Click should play each individual file.

[2] “The Time Machine” – H.G. Wells 

It is Chinese New Year, which runs all this week, so to celebrate the Year of the Rabbit, we asked two AI websites to create original artwork using the phrase “Year of the Rabbit”.  Here are two examples of what was generated by these sites, both of which are free, using the same text.  With considerable new coverage appearing toward AI assisted art and writing applications that the general public is just becoming aware of, we thought it appropriate to generate our Chinese New Year art through such systems, rather than through the usual Google (GOOG) image search.  Are they better, more appropriate, or more to the point than original (human) art?  “Art is in the eye of the beholder, and everyone will have their own interpretation”[1]


[1] E.A. Bucchianeri – “Brushstrokes of a Gadfly”

Magic Leap (pvt), the Florida based AR company that has been one of the most successful AR companies in terms of raising capital, has indicated that the Saudi Arabia Public Investment Fund ($620b as of 1Q ’22) has increased its stake in the company, now holding a controlling interest.  The deal, which consisted of $150m in convertible preferred and $300m in debt, and gives the fund the right to appoint 4 of the company’s 8 directors.  Magic Leap, which was founded in 2010 by Roby Abovitz, has raised over $3.5b through 28 investors, including Google (GOOG), Alibaba (BABA), Qualcomm (QCOM), AT&T (T) and a number of VCs, but went through some difficult times in 2020.  The company was forced to lay off ~1,000 employees (~50% of staff) when sales of its Magic Leap 1 AR headset, released that year failed to materialize as expected, and rumors that the company was looking for a buyer turned out to be untrue as the company went back to private investors for more capital.  The company then changed focus (most of the layoffs came from the consumer segment) to the enterprise market, and the founder and CEO gave up the reins to a new CEO in 2020.  Since then, he has relinquished his board seat to found a new company, Sun & Thunder (pvt), that creates Ai characters (“SynthBees”), a Metaverse engine, and “some really cool and creative things”, according to the company.
Magic Leap’s initial headset problem was price, and while the device (Magic Leap 1) was quite sophisticated, it cost ~$2,300, which was hard to justify in the consumer market in 2018.  Since then the company announced the Magic Leap 2 in January of 2021, but did not release the device until September 29 of this year.  While the new device itself costs ~$3,300 (~$5,000) for the complete package), and is the most expensive AR device released in 2022, it is designed to be used across a number of industry verticals where considerable software has been developed to integrate the device with existing applications such as IT tools, 3D modeling, training, collaboration, technical guidance, and metaverse creation.  The device has considerably more internal storage capacity than similar devices, a positive for quick responses to user needs, although the display uses LCoS (Liquid Crystal on Silicon), an LCD based technology that is rarely used in such devices, having been replaced by Micro-OLED or Micro-LED display technology.
The Saudi confidence in the company is a bit surprising considering their original investment, which we believe was made in March 2018 ($461m) has seen a number of ups and downs but given the pre-money valuation of Magic Leap of between $1.5b and $2b, they seem to be optimistic about the longer-term prospects for AR and Magic Leap itself.  If the Saudi’s do take four board seats, there will have to be some reshuffling, as those seats have been held by Alibaba, Qualcomm, and Google representatives, but with over $700m invested, the Saudi fund has the right to run the board.  All in, the Saudi Fund, which remains under the control of Mohammed bin Salman, the Crown Prince and Prime Minister of Saudi Arabia, has stakes in other tech companies (Uber (UBER), Capcom (9697.JP), etc) but also has made investments in a number of US politically linked companies, particularly those of Jared Kushner, triggering issues surrounding Kushner’s ongoing support for the Saudi government, despite human rights violations.  There has been little controversy over the existing Saudi investment in Magic Leap, but given the political climate, that could change quickly given the company’s status as a US unicorn.,

The State Internet Information Office in China has released a provision relating to the use of “Deep Synthesis of Internet Information”, which more loosely translated would be the use of AI in media services of any type on the internet and in other broadcast venues.  What this refers to is the use of artificial characters that might look like actual persons or are life-like.  This has become a relatively common practice in China, where artificial avatars, who mimic existing media personalities, are used to report the news, or make public commentary.
The new rules force those using such forms of information dissemination to visibly identify them as such, and to monitor the information that is synthesized by AI-based news processors.  In many cases AI is used to sift through large volumes of news from a wide variety of sources and synthesize a short summary of a story that seems to be of consequence.  This is done through the AI system, which looks for recognizable patterns in news flow, and puts together a composite of those stories that it or others have selected by combining source information.
The Chinese government is attempting to move the responsibility for accurate reporting to the media source, making sure that the media owner knows he or she is responsible for making sure the information disseminated is accurate and legal, in that it does not violate existing laws that “endanger national security and interests, damage national image, infringe on social public interests, and disrupt the economy”.  While this seems to be an attempt to ‘keep things real’ by making sure AI-based information is disclosed as such and is legitimate, it also makes clear that the responsibility for meeting all existing laws, some of which are obviously censorship related, are followed, and that blaming misinformation or illegal (meaning different from state rhetoric) information on the AI is not a valid excuse.  Owners of the distribution source must keep concise records of any issues relating to information sources and log and report any discoveries of incorrect or illegal material generated by the AI system, but the rules state that the responsibility to meet applicable laws are the responsibility of the owner, no matter what the AI might broadcast, and that the public must know when Ai generated information is being used.
“For the public good” shows up a number of times in the new regulations, but the rules also seem to have a repressive tone, seemingly extending the government’s ability to suspend, terminate, or prosecute the accounts of those who might find themselves at odds with the state.  Given the recent protests in China, it seems that this is another way to plug up any holes in the media censorship that exists in China, and while it has technology related overtones, it is really a way of reminding the media that it lives under strict rules and technological mistakes or omissions will result in the same repressive results that would occur in any other form of media that exists on the Mainland.
Examples of AI Generated ‘Digital Twins’ are shown below courtesy of Deep Brain AI (pvt), a South Korean firm that is considered the preeminent provider of such technology.
https://renew.deepbrainai.io/renewal_resources/videos/Use+cases+%26+Industries/Media_4_CCTV.mp4
https://renew.deepbrainai.io/renewal_resources/videos/Use+cases+%26+Industries/Media_2_LG.mp4
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