Many of us have struggled for years to master a second language (in my case, English). Because (at least in my situation) we've always used an input-based system or method.

I'll explain in detail, but briefly: We can understand some conversations or sentences (since we've trained), but we can't give sophisticated answers or speak fluently (because we have NOT trained at all).

What exactly is input-based learning?

Reading, listening, writing, and speaking are key language abilities (if you look closely at that list, it seems that people tend to order them in this way: inadvertently giving more priority to the first ones than to the last ones).

These talents fall under two learning styles:

• Reading and listening are input-based activities (sometimes referred to as receptive skills or passive learning).

• Writing and speaking are output-based tasks (also known as the productive skills and/or active learning).

What's the best learning style? To learn a language, we must master four interconnected skills. The difficulty is how much time and effort we give each.

According to Shion Kabasawa's books The Power of Input: How to Maximize Learning and The Power of Output: How to Change Learning to Outcome (available only in Japanese), we spend 7:3 more time on Input Based skills than Output Based skills when we should be doing the opposite, leaning more towards Output (Input: Output->3:7).

I can't tell you how he got those numbers, but I think he's not far off because, for example, think of how many people say they're learning a second language and are satisfied bragging about it by only watching TV, series, or movies in VO (and/or reading a book or whatever) their Input is: 7:0 output!

You can't be good at a sport by watching TikTok videos about it; you must play.

“being pushed to produce language puts learners in a better position to notice the ‘gaps’ in their language knowledge”, encouraging them to ‘upgrade’ their existing interlanguage system. And, as they are pushed to produce language in real time and thereby forced to automate low-level operations by incorporating them into higher-level routines, it may also contribute to the development of fluency. — Scott Thornbury (P is for Push)

How may I practice output-based learning more?

I know that listening or reading is easy and convenient because we can do it on our own in a wide range of situations, even during another activity (although, as you know, it's not ideal), writing can be tedious/boring (it's funny that we almost always excuse ourselves in the lack of ideas), and speaking requires an interlocutor. But we must leave our comfort zone and modify our thinking to go from 3:7 to 7:3. (or at least balance it better to something closer). Gradually.

“You don’t have to do a lot every day, but you have to do something. Something. Every day.” — Callie Oettinger (Do this every day)

We can practice speaking like boxers shadow box.

Speaking out loud strengthens the mind-mouth link (otherwise, you will still speak fluently in your mind but you will choke when speaking out loud). This doesn't mean we should talk to ourselves on the way to work, while strolling, or on public transportation. We should try to do it without disturbing others, such as explaining what we've heard, read, or seen (the list is endless: you can TALK about what happened yesterday, your bedtime book, stories you heard at the office, that new kitten video you saw on Instagram, an experience you had, some new fact, that new boring episode you watched on Netflix, what you ate, what you're going to do next, your upcoming vacation, what’s trending, the news of the day)

Who will correct my grammar, vocabulary, or pronunciation with an imagined friend? We can't have everything, but tools and services can help [1].

Lack of bravery

Fear of speaking a language different than one's mother tongue in front of native speakers is global. It's easier said than done, because strangers, not your friends, will always make fun of your accent or faults. Accept it and try again. Karma will prevail.

Perfectionism is a trap. Stop self-sabotaging. Communication is key (and for that you have to practice the Output too ).

“Don’t forget to have fun and enjoy the process.” — Ruri Ohama

[1] Grammarly, Deepl, Google Translate, etc.

A worldwide energy crisis might have hampered renewable energy and clean tech investment. Nope.

BNEF's 2023 Energy Transition Investment Trends study surprised and encouraged. Global energy transition investment reached $1 trillion for the first time ($1.11t), up 31% from 2021. From 2013, the clean energy transition has come and cannot be reversed.

BNEF Head of Global Analysis Albert Cheung said our findings ended the energy crisis's influence on renewable energy deployment. Energy transition investment has reached a record as countries and corporations implement transition strategies. Clean energy investments will soon surpass fossil fuel investments.

The table below indicates the tripping point, which means the energy shift is occuring today.

BNEF calls money invested on clean technology including electric vehicles, heat pumps, hydrogen, and carbon capture energy transition investment. In 2022, electrified heat received $64b and energy storage $15.7b.

Nonetheless, $495b in renewables (up 17%) and $466b in electrified transport (up 54%) account for most of the investment. Hydrogen and carbon capture are tiny despite the fanfare. Hydrogen received the least funding in 2022 at $1.1 billion (0.1%).

China dominates investment. China spends $546 billion on energy transition, half the global amount. Second, the US total of $141 billion in 2022 was up 11% from 2021. With $180 billion, the EU is unofficially second. China invested 91% in battery technologies.

The 2022 transition tipping point is encouraging, but the BNEF research shows how far we must go to get Net Zero. Energy transition investment must average $4.55 trillion between 2023 and 2030—three times the amount spent in 2022—to reach global Net Zero. Investment must be seven times today's record to reach Net Zero by 2050.

BNEF 2023 Energy Transition Investment Trends.

As shown in the graph above, BNEF experts have been using their crystal balls to determine where that investment should go. CCS and hydrogen are still modest components of the picture. Interestingly, they see nuclear almost fading. Active transport advocates like me may have something to say about the massive $4b in electrified transport. If we focus on walkable 15-minute cities, we may need fewer electric automobiles. Though we need more electric trains and buses.

Albert Cheung of BNEF emphasizes the challenge. This week's figures promise short-term job creation and medium-term energy security, but more investment is needed to reach net zero in the long run.

I expect the BNEF Energy Transition Investment Trends report to show clean tech investment outpacing fossil fuels investment every year. Finally saying that is amazing. It's insufficient. The planet must maintain its electric (not gas) pedal. In response to the research, Christina Karapataki, VC at Breakthrough Energy Ventures, a clean tech investment firm, tweeted: Clean energy investment needs to average more than 3x this level, for the remainder of this decade, to get on track for BNEFs Net Zero Scenario. Go!

Google announced 6% layoffs recently (or 12,000 people). This aligns it with most tech companies. A publicly contrite CEO explained that they had overhired during the COVID-19 pandemic boom and had to address it, but they were sorry and took full responsibility. I thought this was "bullshit" too. Meta, Amazon, Microsoft, and others must feel similarly. I spent 10 years at Google, and these things don't reflect well on the company's leaders.

All publicly listed companies have a fiduciary duty to act in the best interests of their shareholders. Dodge vs. Ford Motor Company established this (1919). Henry Ford wanted to reduce shareholder payments to offer cheaper cars and better wages. Ford stated.

My ambition is to employ still more men, to spread the benefits of this industrial system to the greatest possible number, to help them build up their lives and their homes. To do this we are putting the greatest share of our profits back in the business.

The Dodge brothers, who owned 10% of Ford, opposed this and sued Ford for the payments to start their own company. They won, preventing Ford from raising prices or salaries. If you have a vocal group of shareholders with the resources to sue you, you must prove you are acting in their best interests. Companies prioritize shareholders. Giving activist investors a stick to threaten you almost enshrines short-term profit over long-term thinking.

This underpins Google's current issues. Institutional investors who can sue Google see it as a wasteful company they can exploit. That doesn't mean you have to maximize profits (thanks to those who pointed out my ignorance of US corporate law in the comments and on HN), but it allows pressure. I feel for those navigating this. This is about unrestrained capitalism.

When Google went public, Larry Page and Sergey Brin knew the risks and worked hard to keep control. In their Founders' Letter to investors, they tried to set expectations for the company's operations.

Our long-term focus as a private company has paid off. Public companies do the same. We believe outside pressures lead companies to sacrifice long-term opportunities to meet quarterly market expectations.

The company has transformed since that letter. The company has nearly 200,000 full-time employees and a trillion-dollar market cap. Large investors have bought company stock because it has been a good long-term bet. Why are they restless now?

Other big tech companies emerged and fought for top talent. This has caused rising compensation packages. Google has also grown rapidly (roughly 22,000 people hired to the end of 2022). At $300,000 median compensation, those 22,000 people added $6.6 billion in salary overheads in 2022. Exorbitant. If the company still makes $16 billion every quarter, maybe not. Investors wonder if this value has returned.

Investors are right. Google uses people wastefully. However, by bluntly reducing headcount, they're not addressing the root causes and hurting themselves. No studies show that downsizing this way boosts productivity. There is plenty of evidence that they'll lose out because people will be risk-averse and distrust their leadership.

The company's approach also stinks. Finding out that you no longer have a job because you can’t log in anymore (sometimes in cases where someone is on call for protecting your production systems) is no way to fire anyone. Being with a narcissistic sociopath is like being abused. First, you receive praise and fancy perks for making the cut. You're fired by text and ghosted. You're told to appreciate the generous severance package. This firing will devastate managers and teams. This type of firing will take years to recover self-esteem. Senior management contributed to this. They chose the expedient answer, possibly by convincing themselves they were managing risk and taking the Macbeth approach of “If it were done when ’tis done, then ’twere well It were done quickly”.

Recap. Google's leadership did a stupid thing—mass firing—in a stupid way. How do we get rid of enough people to make investors happier? and "have 6% less people." Empathetic leaders should not emulate Elon Musk. There is no humane way to fire 12,000 people, but there are better ways. Why is Google so wasteful?

Ambition answers this. There aren't enough VP positions for a group of highly motivated, ambitious, and (increasingly) ruthless people. I’ve loitered around the edges of this world and a large part of my value was to insulate my teams from ever having to experience it. It’s like Game of Thrones played out through email and calendar and over video call.

Your company must look a certain way to be promoted to director or higher. You need the right people at the right levels under you. Long-term, growing your people will naturally happen if you're working on important things. This takes time, and you're never more than 6–18 months from a reorg that could start you over. Ambitious people also tend to be impatient. So, what do you do?

Hiring and vanity projects. To shape your company, you hire at the right levels. You value vanity metrics like active users over product utility. Your promo candidates get through by subverting the promotion process. In your quest for growth, you avoid performance managing people out. You avoid confronting toxic peers because you need their support for promotion. Your cargo cult gets you there.

Its ease makes Google wasteful. Since they don't face market forces, the employees don't see it as a business. Why would you do when the ads business is so profitable? Complacency causes senior leaders to prioritize their own interests. Empires collapse. Personal ambition often trumped doing the right thing for users, the business, or employees. Leadership's ambition over business is the root cause. Vanity metrics, mass hiring, and vague promises have promoted people to VP. Google goes above and beyond to protect senior leaders.

The decision-makers and beneficiaries are not the layoffees. Stock price increase beneficiaries. The people who will post on LinkedIn how it is about misjudging the market and how they’re so sorry and take full responsibility. While accumulating wealth, the dark room dwellers decide who stays and who goes. The billionaire investors. Google should start by addressing its bloated senior management, but — as they say — turkeys don't vote for Christmas. It should examine its wastefulness and make tough choices to fix it. A 6% cut is a blunt tool that admits you're not running your business properly. why aren’t the people running the business the ones shortly to be entering the job market?

This won't fix Google's wastefulness. The executives may never regain trust after their approach. Suppressed creativity. Business won't improve. Google will have lost its founding vision and us all. Large investors know they can force Google's CEO to yield. The rich will get richer and rationalize leaving 12,000 people behind. Cycles repeat.

It doesn’t have to be this way. In 2013, Nintendo's CEO said he wouldn't fire anyone for shareholders. Switch debuted in 2017. Nintendo's stock has increased by nearly five times, or 19% a year (including the drop most of the stock market experienced last year). Google wasted 12,000 talented people. To please rich people.

Deep learning-based generative art is being researched. As usual, self-learning is better. Some models, like OpenAI's DALL-E 2, require registration and can only be used online, but others can be used locally, which is usually more enjoyable for curious users. I'll demonstrate the Stable Diffusion model's operation on a standard PC.

Let’s get started.

What It Does

Stable Diffusion uses numerous components:

• A generative model trained to produce images is called a diffusion model. The model is incrementally improving the starting data, which is only random noise. The model has an image, and while it is being trained, the reversed process is being used to add noise to the image. Being able to reverse this procedure and create images from noise is where the true magic is (more details and samples can be found in the paper).

• An internal compressed representation of a latent diffusion model, which may be altered to produce the desired images, is used (more details can be found in the paper). The capacity to fine-tune the generation process is essential because producing pictures at random is not very attractive (as we can see, for instance, in Generative Adversarial Networks).

• A neural network model called CLIP (Contrastive Language-Image Pre-training) is used to translate natural language prompts into vector representations. This model, which was trained on 400,000,000 image-text pairs, enables the transformation of a text prompt into a latent space for the diffusion model in the scenario of stable diffusion (more details in that paper).

This figure shows all data flow:

The weights file size for Stable Diffusion model v1 is 4 GB and v2 is 5 GB, making the model quite huge. The v1 model was trained on 256x256 and 512x512 LAION-5B pictures on a 4,000 GPU cluster using over 150.000 NVIDIA A100 GPU hours. The open-source pre-trained model is helpful for us. And we will.

Install

Before utilizing the Python sources for Stable Diffusion v1 on GitHub, we must install Miniconda (assuming Git and Python are already installed):

wget https://repo.anaconda.com/miniconda/Miniconda3-py39_4.12.0-Linux-x86_64.sh
chmod +x Miniconda3-py39_4.12.0-Linux-x86_64.sh
./Miniconda3-py39_4.12.0-Linux-x86_64.sh
conda update -n base -c defaults conda

Install the source and prepare the environment:

git clone https://github.com/CompVis/stable-diffusion
cd stable-diffusion
conda env create -f environment.yaml
conda activate ldm
pip3 install transformers --upgrade

Download the pre-trained model weights next. HiggingFace has the newest checkpoint sd-v14.ckpt (a download is free but registration is required). Put the file in the project folder and have fun:

python3 scripts/txt2img.py --prompt "hello world" --plms --ckpt sd-v1-4.ckpt --skip_grid --n_samples 1

Almost. The installation is complete for happy users of current GPUs with 12 GB or more VRAM. RuntimeError: CUDA out of memory will occur otherwise. Two solutions exist.

Running the optimized version

Try optimizing first. After cloning the repository and enabling the environment (as previously), we can run the command:

python3 optimizedSD/optimized_txt2img.py --prompt "hello world" --ckpt sd-v1-4.ckpt --skip_grid --n_samples 1

Stable Diffusion worked on my visual card with 8 GB RAM (alas, I did not behave well enough to get NVIDIA A100 for Christmas, so 8 GB GPU is the maximum I have;).

Running Stable Diffusion without GPU

If the GPU does not have enough RAM or is not CUDA-compatible, running the code on a CPU will be 20x slower but better than nothing. This unauthorized CPU-only branch from GitHub is easiest to obtain. We may easily edit the source code to use the latest version. It's strange that a pull request for that was made six months ago and still hasn't been approved, as the changes are simple. Readers can finish in 5 minutes:

• Replace if attr.device!= torch.device(cuda) with if attr.device!= torch.device(cuda) and torch.cuda.is available at line 20 of ldm/models/diffusion/ddim.py ().

• Replace if attr.device!= torch.device(cuda) with if attr.device!= torch.device(cuda) and torch.cuda.is available in line 20 of ldm/models/diffusion/plms.py ().

• Replace device=cuda in lines 38, 55, 83, and 142 of ldm/modules/encoders/modules.py with device=cuda if torch.cuda.is available(), otherwise cpu.

• Replace model.cuda() in scripts/txt2img.py line 28 and scripts/img2img.py line 43 with if torch.cuda.is available(): model.cuda ().

Run the script again.

Testing

Test the model. Text-to-image is the first choice. Test the command line example again:

python3 scripts/txt2img.py --prompt "hello world" --plms --ckpt sd-v1-4.ckpt --skip_grid --n_samples 1

The slow generation takes 10 seconds on a GPU and 10 minutes on a CPU. Final image:

Hello world is dull and abstract. Try a brush-wielding hamster. Why? Because we can, and it's not as insane as Napoleon's cat. Another image:

Generating an image from a text prompt and another image is interesting. I made this picture in two minutes using the image editor (sorry, drawing wasn't my strong suit):

I can create an image from this drawing:

python3 scripts/img2img.py --prompt "A bird is sitting on a tree branch" --ckpt sd-v1-4.ckpt --init-img bird.png --strength 0.8

It was far better than my initial drawing:

I hope readers understand and experiment.

Stable Diffusion UI

Developers love the command line, but regular users may struggle. Stable Diffusion UI projects simplify image generation and installation. Simple usage:

• Unpack the ZIP after downloading it from https://github.com/cmdr2/stable-diffusion-ui/releases. Linux and Windows are compatible with Stable Diffusion UI (sorry for Mac users, but those machines are not well-suitable for heavy machine learning tasks anyway;).

• Start the script.

Done. The web browser UI makes configuring various Stable Diffusion features (upscaling, filtering, etc.) easy:

V2.1 of Stable Diffusion

I noticed the notification about releasing version 2.1 while writing this essay, and it was intriguing to test it. First, compare version 2 to version 1:

• alternative text encoding. The Contrastive LanguageImage Pre-training (CLIP) deep learning model, which was trained on a significant number of text-image pairs, is used in Stable Diffusion 1. The open-source CLIP implementation used in Stable Diffusion 2 is called OpenCLIP. It is difficult to determine whether there have been any technical advancements or if legal concerns were the main focus. However, because the training datasets for the two text encoders were different, the output results from V1 and V2 will differ for the identical text prompts.

• a new depth model that may be used to the output of image-to-image generation.

• a revolutionary upscaling technique that can quadruple the resolution of an image.

• Generally higher resolution Stable Diffusion 2 has the ability to produce both 512x512 and 768x768 pictures.

The Hugging Face website offers a free online demo of Stable Diffusion 2.1 for code testing. The process is the same as for version 1.4. Download a fresh version and activate the environment:

conda deactivate  
conda env remove -n ldm  # Use this if version 1 was previously installed
git clone https://github.com/Stability-AI/stablediffusion
cd stablediffusion
conda env create -f environment.yaml
conda activate ldm

Hugging Face offers a new weights ckpt file.

The Out of memory error prevented me from running this version on my 8 GB GPU. Version 2.1 fails on CPUs with the slow conv2d cpu not implemented for Half error (according to this GitHub issue, the CPU support for this algorithm and data type will not be added). The model can be modified from half to full precision (float16 instead of float32), however it doesn't make sense since v1 runs up to 10 minutes on the CPU and v2.1 should be much slower. The online demo results are visible. The same hamster painting with a brush prompt yielded this result:

It looks different from v1, but it functions and has a higher resolution.

The superresolution.py script can run the 4x Stable Diffusion upscaler locally (the x4-upscaler-ema.ckpt weights file should be in the same folder):

python3 scripts/gradio/superresolution.py configs/stable-diffusion/x4-upscaling.yaml x4-upscaler-ema.ckpt

This code allows the web browser UI to select the image to upscale:

The copy-paste strategy may explain why the upscaler needs a text prompt (and the Hugging Face code snippet does not have any text input as well). I got a GPU out of memory error again, although CUDA can be disabled like v1. However, processing an image for more than two hours is unlikely:

Stable Diffusion Limitations

When we use the model, it's fun to see what it can and can't do. Generative models produce abstract visuals but not photorealistic ones. This fundamentally limits The generative neural network was trained on text and image pairs, but humans have a lot of background knowledge about the world. The neural network model knows nothing. If someone asks me to draw a Chinese text, I can draw something that looks like Chinese but is actually gibberish because I never learnt it. Generative AI does too! Humans can learn new languages, but the Stable Diffusion AI model includes only language and image decoder brain components. For instance, the Stable Diffusion model will pull NO WAR banner-bearers like this:

V1:

V2.1:

The shot shows text, although the model never learned to read or write. The model's string tokenizer automatically converts letters to lowercase before generating the image, so typing NO WAR banner or no war banner is the same.

I can also ask the model to draw a gorgeous woman:

V1:

V2.1:

The first image is gorgeous but physically incorrect. A second one is better, although it has an Uncanny valley feel. BTW, v2 has a lifehack to add a negative prompt and define what we don't want on the image. Readers might try adding horrible anatomy to the gorgeous woman request.

If we ask for a cartoon attractive woman, the results are nice, but accuracy doesn't matter:

V1:

V2.1:

Another example: I ordered a model to sketch a mouse, which looks beautiful but has too many legs, ears, and fingers:

V1:

V2.1: improved but not perfect.

V1 produces a fun cartoon flying mouse if I want something more abstract:

I tried multiple times with V2.1 but only received this:

The image is OK, but the first version is closer to the request.

Stable Diffusion struggles to draw letters, fingers, etc. However, abstract images yield interesting outcomes. A rural landscape with a modern metropolis in the background turned out well:

V1:

V2.1:

Generative models help make paintings too (at least, abstract ones). I searched Google Image Search for modern art painting to see works by real artists, and this was the first image:

I typed "abstract oil painting of people dancing" and got this:

V1:

V2.1:

It's a different style, but I don't think the AI-generated graphics are worse than the human-drawn ones.

The AI model cannot think like humans. It thinks nothing. A stable diffusion model is a billion-parameter matrix trained on millions of text-image pairs. I input "robot is creating a picture with a pen" to create an image for this post. Humans understand requests immediately. I tried Stable Diffusion multiple times and got this:

This great artwork has a pen, robot, and sketch, however it was not asked. Maybe it was because the tokenizer deleted is and a words from a statement, but I tried other requests such robot painting picture with pen without success. It's harder to prompt a model than a person.

I hope Stable Diffusion's general effects are evident. Despite its limitations, it can produce beautiful photographs in some settings. Readers who want to use Stable Diffusion results should be warned. Source code examination demonstrates that Stable Diffusion images feature a concealed watermark (text StableDiffusionV1 and SDV2) encoded using the invisible-watermark Python package. It's not a secret, because the official Stable Diffusion repository's test watermark.py file contains a decoding snippet. The put watermark line in the txt2img.py source code can be removed if desired. I didn't discover this watermark on photographs made by the online Hugging Face demo. Maybe I did something incorrectly (but maybe they are just not using the txt2img script on their backend at all).

Conclusion

The Stable Diffusion model was fascinating. As I mentioned before, trying something yourself is always better than taking someone else's word, so I encourage readers to do the same (including this article as well;).

Is Generative AI a game-changer? My humble experience tells me:

• I think that place has a lot of potential. For designers and artists, generative AI can be a truly useful and innovative tool. Unfortunately, it can also pose a threat to some of them since if users can enter a text field to obtain a picture or a website logo in a matter of clicks, why would they pay more to a different party? Is it possible right now? unquestionably not yet. Images still have a very poor quality and are erroneous in minute details. And after viewing the image of the stunning woman above, models and fashion photographers may also unwind because it is highly unlikely that AI will replace them in the upcoming years.

• Today, generative AI is still in its infancy. Even 768x768 images are considered to be of a high resolution when using neural networks, which are computationally highly expensive. There isn't an AI model that can generate high-resolution photographs natively without upscaling or other methods, at least not as of the time this article was written, but it will happen eventually.

• It is still a challenge to accurately represent knowledge in neural networks (information like how many legs a cat has or the year Napoleon was born). Consequently, AI models struggle to create photorealistic photos, at least where little details are important (on the other side, when I searched Google for modern art paintings, the results are often even worse;).

• When compared to the carefully chosen images from official web pages or YouTube reviews, the average output quality of a Stable Diffusion generation process is actually less attractive because to its high degree of randomness. When using the same technique on their own, consumers will theoretically only view those images as 1% of the results.

Anyway, it's exciting to witness this area's advancement, especially because the project is open source. Google's Imagen and DALL-E 2 can also produce remarkable findings. It will be interesting to see how they progress.

Bear markets don't last forever, but that's hard to remember. Jamie Cullen's illustration

A bear market is a 20% decline from peak to trough in stock prices.

The S&P 500 was down 24% from its January highs at its low point this year. Bear market.

The U.S. stock market has had 13 bear markets since WWII (including the current one). Previous 12 bear markets averaged –32.7% losses. From peak to trough, the stock market averaged 12 months. The average time from bottom to peak was 21 months.

In the past seven decades, a bear market roundtrip to breakeven has averaged less than three years.

Long-term averages can vary widely, as with all historical market data. Investors can learn from past market crashes.

Historical bear markets offer lessons.

Bear market duration

A bear market can cost investors money and time. Most of the pain comes from stock market declines, but bear markets can be long.

Here are the longest U.S. stock bear markets since World war 2:

Stock market crashes can make it difficult to break even. After the 2008 financial crisis, the stock market took 4.5 years to recover. After the dotcom bubble burst, it took seven years to break even.

The longer you're underwater in the market, the more suffering you'll experience, according to research. Suffering can lead to selling at the wrong time.

Bear markets require patience because stocks can take a long time to recover.

Stock crash recovery

Bear markets can end quickly. The Corona Crash in early 2020 is an example.

The S&P 500 fell 34% in 23 trading sessions, the fastest bear market from a high in 90 years. The entire crash lasted one month. Stocks broke even six months after bottoming. Stocks rose 100% from those lows in 15 months.

Seven bear markets have lasted two years or less since 1945.

The 2020 recovery was an outlier, but four other bear markets have made investors whole within 18 months.

During a bear market, you don't know if it will end quickly or feel like death by a thousand cuts.

Recessions vs. bear markets

Many people believe the U.S. economy is in or heading for a recession.

I agree. Four-decade high inflation. Since 1945, inflation has exceeded 5% nine times. Each inflationary spike caused a recession. Only slowing economic demand seems to stop price spikes.

This could happen again. Stocks seem to be pricing in a recession.

Recessions almost always cause a bear market, but a bear market doesn't always equal a recession. In 1946, the stock market fell 27% without a recession in sight. Without an economic slowdown, the stock market fell 22% in 1966. Black Monday in 1987 was the most famous stock market crash without a recession. Stocks fell 30% in less than a week. Many believed the stock market signaled a depression. The crash caused no slowdown.

Economic cycles are hard to predict. Even Wall Street makes mistakes.

Bears vs. bulls

Bear markets for U.S. stocks always end. Every stock market crash in U.S. history has been followed by new all-time highs.

How should investors view the recession? Investing risk is subjective.

You don't have as long to wait out a bear market if you're retired or nearing retirement. Diversification and liquidity help investors with limited time or income. Cash and short-term bonds drag down long-term returns but can ensure short-term spending.

Young people with years or decades ahead of them should view this bear market as an opportunity. Stock market crashes are good for net savers in the future. They let you buy cheap stocks with high dividend yields.

You need discipline, patience, and planning to buy stocks when it doesn't feel right.

Bear markets aren't fun because no one likes seeing their portfolio fall. But stock market downturns are a feature, not a bug. If stocks never crashed, they wouldn't offer such great long-term returns.