The term Metaverse exploded into the public lexicon with the rebranding of Facebook to Meta. Since then, the Metaverse has become a trending topic, the catchphrase of CES, and something of a meme. While there seems to be a common acceptance that the Metaverse is real – or will be – there are still many questions and uncertainty of what the Metaverse is, how the metaverse words, and why it’s important.

As a leading AI app developer, we’re laser-focused on staying ahead of what new technologies are coming to the market, understanding how these new technologies will influence your business and what implications these new technologies have on the app development space as a whole.

So, to deliver on staying on top of these new evolving technologies, we thought it would be useful to dig into what exactly the metaverse is, and why it’s important for you to understand this significant technological trend.

What is the Metaverse

The Metaverse may be catching on in social and tech circles, but the term goes back to 1992 when it was used in a book titled Snow Crash by author Neal Stephenson. The Metaverse in the book is described as having life-like avatars interacting with each other in a three-dimensional virtual reality world. The Metaverse of today could look and feel very similar, and there have been elements of this VR world found in modern digital experience.

 A real-world version of the Metaverse intends to combine virtual reality and augmented reality to create a digital world where real people depicted as digital avatars can interact with each other and the virtual environment. The height of the vision for the Metaverse is a place where you could work, play, explore, and travel virtually with almost anyone from almost anywhere. The bedrock of this somewhat nebulous vision is a reimagining of the internet and what it means to connect to each other digitally.

Why the Metaverse Matters

Once you get past the possibilities and appeal that the metaverse promises, it is not hard to imagine how the Metaverse can be used for commerce and profit. Microsoft and Meta are investing heavily in the Metaverse, and these companies and others have established virtual and augmented reality hardware and software already on the market.

 Buying and selling digital goods and services are far from a new concept. The video game industry is a prime example. A standard business model is for game development and publishing companies to offer their games for free, banking on the in-game purchasing of digital items and currency that change the look of a player’s character and accessories. In some cases, these digital purchases provide paid advantages over non-paying players.

 Beyond just video games, the rise in non-fungible tokens (NFTs) and cryptocurrency will have a significant stake in the economy of the Metaverse.

So, why does this matter for businesses? Well, those companies that are acting early and investing in digital experiences or apps to position around this adoption of the Metaverse, may win big if the Metaverse truly grows to become what its proponents suggest. For instance, many clients seeking an iPhone app developer in Los Angeles have reached out to inquire how they can position their app development strategy to align with this insatiable buzz of the Metaverse.

NFTs, Blockchain, and the Metaverse

Blockchain and NFTs currently exist in the digital world and are compelling alternative payment forms compared to traditional currency. Both will be a pillar of whatever the Metaverse becomes. Blockchain is the foundation that cryptocurrency is built on. A blockchain is a distributed database shared across a computer network. Because blockchains are immutable ledgers that can’t be edited or deleted, that makes them the perfect storage for cryptocurrencies and NFTs.

 Like a blockchain, or more accurately because of it, NFTs have become a very popular way to represent digital ownership on the blockchain. NFTs are heavily used in the video game industry, and a new subset of the sector is blockchain gaming. The concept allows players to turn their progress in the game into cryptocurrency through NFTs.

 As the Metaverse will rely heavily on the blockchain, it’s easy to see how cryptocurrency and NFTs will be the digital assets of choice for buying and selling in the digital world. It is not hard to imagine, for example, a new definition and direction for the traditional real estate industry.

 Instead of buying a brick-and-mortar home, a real estate agent, using a virtual avatar, will show clients a three-dimensional virtual home. The agent and the buyer – also using a virtual avatar – would complete the sale using cryptocurrency and ownership of the digital home established using NFTs. This hypothetical example sounds like a video game, but it could be real and here sooner than we think.

Moving Forward

In some ways, the Metaverse is already here, and many people engage with it daily. The future of the Metaverse, what shape it will take, and the further immersion of our real world with a fully-realized digital one, is only a few years away.

As the 2021 year comes to a close and we anticipate what’s to come in 2022, it’s with great appreciation and honor to announce that Dogtown Media has received a global accolade from the major digital rating agency, Clutch.co, as a Top Global B2B Company for 2021.

After 10 years in the mobile app space, reaching a highly regarded and recognized global accolade is a major accomplishment and points to the continued dedication of Dogtown Media to their global client base and their hyper-focus on producing high-quality applications. 

Dogtown Media is Los Angeles’ leading mobile application company, working with organizations in nearly every vertical to bring their unique ideas and solutions to the app market. Dogtown Media prides itself on the satisfaction, approval, and happiness of our clients. And aims to create cutting-edge solutions that are pushing the boundaries of what’s thought o be possible in the mobile application space.

the mobile application space.

And for those who may be unaware, this Clutch.co accolade is only one in a series of major accolades awarded to Dogtown Media such as Top 2021 B2B Leader in Artificial Intelligence for Robotics, a top 2020 Service Provider, and the 27th Best B2B Service Provider in the World in 2019. All of these great accolades point to the dedication to craft and customer, and only scratch the surface of their long laundry list of accolades from Clutch and other prominent rating agencies in the mobile app space. 

“This recognition feels surreal and we are lost for words”, notes founder Marc Fischer. “We feel truly honored to be recognized by such a prestigious rating firm, and hope to continue to provide high-quality, meaningful applications for our clients today and far into the future. “

Here are some of the quotes that stood out most to us:

Here are some of the quotes that stood out most to us:

“They were an effective team, met deadlines, and created a great end product.“. — Director, Risk Comm Lab, Temple University

“They built an intuitive and simple design, and the team works quickly to address bugs and solve problems.”— Senior Ops Manager, Hospital Innovation Lab

Let’s build something amazing together! Connect with us and get a free tech consultation.

The goal of robotics is to develop and construct meaningful machines that will support and help human processes. The multidisciplinary field fuses technologies such as artificial intelligence and machine learning together to develop innovative solutions.

Dogtown Media is Los Angeles’ leading robotics company, working with enterprises and organizations to help their businesses. Our team prides itself on the satisfaction, approval, and happiness of our clients. We want to create cutting-edge solutions to solve simple frustrations and tackle business hurdles.

Just recently, Dogtown Media was hailed as a top agency on Clutch for its excellence in AI for robotics. If you’re not familiar, Clutch is a B2B review platform based in Washington, DC. The site is well respected in the space for its commitment to providing data-driven content and verifying client reviews.

This recognition feels surreal and we are lost for words. Our team wants to send its sincerest thanks to Clutch for this award. We believe that this award is a great sign for our 2021 run, and we are looking forward to a prosperous year. 

 We know that this recognition was made possible thanks to our clients’ amazing feedback. We owe this success to our clients especially those who left us their review on Clutch. 

Here are some of the quotes that stood out most to us:

“They’re a small shop that’s motivated and offers a comprehensive list of services and capabilities. They’re accountable and willing to work by our side to make the best product possible. The entire team is professional and eager to solve our problems.” — Founder, Mobile Sales Training Company

“Dogtown Media developed a solution for a project considered impossible to do in the tech world. They’ve taken every goal we’ve had and delivered above and beyond our expectations, beating our requirement of achieving a 50% accuracy rating on visual search capabilities with a software that is over 90% accurate.” — CTO, Innovengine

Let’s build something amazing together! Connect with us and get a free tech consultation.

By now, we’re all aware that the development of artificial intelligence (AI) and machine learning will shape our future in several ways. But many of us do not know how these technologies will impact cybersecurity.

We find ourselves at a pivotal moment in this digital era — one in which our personal information is at unprecedented risk. The last decade alone was riddled with hundreds of massive data breaches and identity fraud incidents. Today, cyber criminals can achieve their objectives from anywhere in the world, at any time.

Our need for more progressive cybersecurity measures has never been more imperative than now. Fortunately, cybersecurity applications have received numerous technological advancements over the last few years. Chief among these game-changing developments is the introduction of AI and machine learning to the field. Let’s examine how these technologies are augmenting current cybersecurity endeavors.

1. Improving Cyber Threat Detection With Machine Learning

In cybersecurity, foresight is priceless. Detecting cyber attacks in advance can give organizations the time they need to successfully neutralize these incoming threats. And it turns out that the application of machine learning to data analysis can help immensely in identifying them.

Machine learning helps computers learn from and understand obtained data. In turn, systems can adjust and refine algorithms to reach optimal performance. In terms of cybersecurity, this means that machine learning can enable computers to detect threats and anomalies more accurately than any human is capable of doing.

Traditional technology relies too much upon previous results. Often, this leaves it unable to adapt fast enough to hackers’ latest techniques and strategies. And the sheer volume of cyber threats that people face every day is too immense for human-directed systems. On the other hand, AI allows computers to excel at improvisation by adapting faster than ever before.

2. AI-Fueled Phishing Detection and Prevention

Phishing is the fraudulent practice of sending fake messages. Hackers use this all the time; they pretend to be from reputable organizations or groups so that victims either reveal personal information like passwords or install malware. Phishing emails are so common that one in every 99 email messages is believed to be an attempted attack.

Luckily, AI and machine learning play an integral role in mitigating phishing attacks. Besides being able to respond much faster than a human can, these technologies can identify and track over 10,000 active phishing sources. They also allow for swift distinction between fake and valid websites. Because these technologies are now being employed around the world, AI’s knowledge of phishing campaigns isn’t relegated to only one geographic location.

3. Making Vulnerability Management Easier

Every modern business relies on information technology (IT). But keeping your IT safe can be difficult. Just this year, over 2,000 unique cybersecurity vulnerabilities have been recorded. Managing these with only humans would be practically impossible. Thankfully, AI opens up an easier approach.

AI- and machine learning-based systems can efficiently scan for potential flaws in corporate IT systems. And, by incorporating recent relevant information such as dark web forums, hacking trends, and more, these technologies make it simple to stay on top of the latest developments in this field. With all of these insights, you’ll not only know how your vulnerable targets may be attacked, but also when.

4. More Powerful Password Protection and Authentication

Passwords have always been one of the weakest components of security control. In fact, they’re often the only link between cyber criminal activity and our identities. Biometric authentication is seen as a potential alternative for the future, but it’s currently not the most convenient paradigm to employ. AI could change this.

Developers are leveraging AI to improve biometric authentication and eliminate any weaknesses so that it’s more robust. Apple’s facial recognition technology is a prime example of this. Known as Face ID, this system detects a user’s facial features via infrared sensors. Apple’s AI software then produces a sophisticated representation of the user’s face that allows it to recognize key similarities.

Apple is so confident in this technology that it believes hackers have a one-in-a-million probability of bypassing it. This system also works under different lighting conditions and can compensate for changes such as a new hairstyle or more facial hair.

5. Automated Network Security

Security policy development and organization network topography are two essential components of network security. Unfortunately, both take up a monumental amount of time and human effort to fulfill and manage.

Fortunately, AI can automate both of these processes to some degree. By analyzing network traffic dynamics, AI can generate and recommend policies and procedures to fit your unique situation. The amount of time, energy, and money this could save organizations can’t be overstated.

6. More Robust Behavioral Analytics

Similar to our other examples, AI and machine learning can also be employed to improve behavioral analytics by studying your patterns. This allows them to understand how you use your computer and other smart devices. Details can include but are not limited to your favorite online platforms, usual login times, as well as your texting and browsing patterns.

If an algorithm detects unusual actions that are outside your normal patterns, it can lock the culprit of this questionable activity out of your system. Massive shopping sprees, shipping products to addresses other than your own (e.g., why’d you ship that new game console to Beijing if you live in Los Angeles?), a sharp spike in uploads or downloads of files, and even a change in your typing pace can all alert AI to nefarious behavior.

AI and ML Make Smarter Cybersecurity Possible

We hope you’ve enjoyed this list of amazing ways that AI and machine learning are improving cybersecurity. As far as security goes, these emerging technologies have vast potential for sectors such as finance, retail, and healthcare.

Speaking of different industries, stay tuned for our follow-up post to this article! We’ll delve into how AI is preventing data breaches in three large sectors.

In the meantime, what do you think of AI and cybersecurity together? And what cybersecurity measures do you employ to protect your information? As always, let us know your thoughts in the comments below!

Automatic clipping hanging machine with pack for sausages

Warning: This article serves as an industrial use case study for how meatpacking facilities are applying automation to improve efficiency and effectiveness. Consequently, many of the images in this post contain depictions of these meat processing operations that some would consider graphic. If this statement leaves you feeling squeamish, you may want to skip this one!

Grocery shoppers from Los Angeles to New York have noticed a decrease in meat on the shelves combined with higher prices for anything that’s in stock. The COVID-19 pandemic has hit meatpacking facilities hard, with thousands of workers getting sick and more than 30 dying. These facilities are cold, humid, and cramped, requiring workers to stand shoulder-to-shoulder working so fast that washing hands to adjust a face mask isn’t even possible.

With the help of the Internet of Things, an innovative technology that’s already improving business operations in industrial facilities all over the world, meatpacking factories can start to automate processes to reduce the number of employees needed on the floor at any given time.

Business As Usual During Uncertain Times

During the COVID-19 pandemic, dozens of meat processing facilities have closed or slowed down operations. Grocery stores are now rationing meats. A Milwaukee-based sausage factory stopped manufacturing its hot dogs completely. But in Europe’s biggest pig slaughterhouse, the factory floor is completely automated. Robots do most of the work while being overseen by humans.

At this facility, everything is business as usual. Pigs come in on trucks around 5:30 AM. Workers herd the pigs into temporary pens on the 90-acre facility. A few hours later, a few pigs are nudged out of the pens and into CO2 gas chambers. The gas knocks them out, and they’re put onto a belt that helps position them for a worker to cut off each pig’s rear feet. From there, the pigs are transported to a moving production line. A worker cuts the pig’s carotid artery, and a vacuum is placed at the cut to lap up all of the blood.

After that, it’s up to the robots to finish the job. A laser-enabled robot measures the pig’s dimensions. Using those numbers, another robot cuts a 4-inch hole around the pig’s tail with a customized artificial intelligence (AI) application. It then reaches into the pig to take out any excrement that’s left inside the carcass. A bladed robot then cuts the pig from top to bottom before a robot mechanically removes organs, slashes tendons, and splits the spine.

The robot portion of the pig’s journey takes only ten minutes. At the end of the working day (midnight), a total of 18,000 pigs have been slaughtered, cleaned, excavated, and packaged.

Modernizing An Age-Old Process

This facility is one of the world’s largest, most modern, and most transparent meat processing facilities in the world.

In fact, many Danish slaughterhouses are heavily automated using IoT applications, and it’s helped them prevent becoming hotspots for COVID-19 infections. Out of 8,000 of the company’s Danish employees, less than 10 have tested positive for the virus. In stark contrast to America (and the rest of the world), none of the Danish slaughterhouses closed down or slowed production.

Meat scientists say that others in this sector and other industrial markets should take note of the Danish slaughterhouses. They’re a prime example of how to physically and technologically set up a facility for long-term success. Robots, IoT, and AI work together with humans to make meatpacking safer and more efficient.

Jayson Lusk is a food and agricultural economist at Purdue University. He says that the cost of not investing in IoT has come back as a bad consequence of failing to implement emerging technologies. According to Lusk, the US government doesn’t provide economic incentives to automate meat processing plants.

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As a result, meat facilities have employed undocumented workers instead of investing in new technologies. In the US, staying competitive as a meat facility depends on how many workers you have tending to the production line.

Progressing Forward Faster

Labor shortages are forcing meat facilities to change their business operations from the ground up. Poultry facilities in the US have become more automated for the last few decades; it took an hour to process 3,000 chickens in 1970, and we can process 15,000 chickens per hour today. But because chickens are much smaller than pigs and cows, the investment to automate is a lot lower.

In the past decade, pork and beef facilities have started introducing more automation into their factories. In 2018, a new pork facility in Michigan opened with automation and robots. It helped the company cut 300 employees while increasing output. The plant has been able to stay open through the pandemic, but it’s slowed down production because it ordered new protective equipment to install.

Close up bologna sliced plate on conveyor of automatic slicer machine for industrial food manufacture

Last year, Tyson Foods start investing heavily in robots for its pork facilities, in an effort to reduce the negative effects of labor shortages.

Preventing the Inevitable

Even if every meat plant in the world automated their production lines and installed robots overnight, it might still not prevent pathogens like COVID-19 from spreading throughout the factory.

Without a strong healthcare system, it’s impossible to prevent a contagion like COVID-19. Workers need to be able to work from home or stay at home when they’re sick without any worry about losing their job and health insurance.

It’s also extremely important that the government moves quickly when a pandemic or any global problem occurs. In the US, President Trump signed an executive order that made meat a critical and scarce “material essential to the national defense”. It meant that meat facilities stayed open while facing less legal liability, even if their workers got sick.

Prioritizing Worker Safety Is a Must

If lowering the number of people in a factory can lower the risk of infection, installing a few robots can help bring down the number of cases.

But automation takes time to implement and optimize. So, in the meantime, factories need to invest heavily in their employees, all of whom keep the factories running at full speed at the risk of their own health and lives. Without more value placed on worker safety and lives, it seems that robots may not help prevent the next pandemic at all.

Hopefully, other industrial facilities examine and learn some insights from the precedent set forth by Danish Crown’s meat processing facilities. What do you think of IoT-fueled automation? As always, let us know your thoughts in the comments below!

Robotic medicine artificial intelligence concept. Robot doctor with stethoscope, syringe blood test. Blue background. empty space for text.

There are many ways humanity is mobilizing to fight against the COVID-19 crisis. While most of us can’t help out at the hospital as a nurse or doctor, we can sew masks, use our 3D printers to make supplementary PPE, and create web and mobile resources to help others cope.

Luckily, we’re also receiving a helping hand from technology. It turns out that robots are doing their fair share of aid: more than two dozen types of robots have been working to support public health and safety by working inside and outside of hospitals, automating testing, and helping humans slowly return to normal life again.

The Newest Essential Workers

If you’ve been to a hospital lately, you might have seen a cylindrical robot wheeling around the ICU area. This automaton allows healthcare workers to take the patient’s temperature, oxygen saturation, and blood pressure remotely. It also helps the patient (especially those on ventilators) maintain their breathing in a sterile and sanitized room.

But this isn’t the only robot concerned with cleanliness. Many medical facilities are also employing large robots equipped with ultraviolet light; they roam the hallways, rotating vertically to disinfect everything they pass by.

Robots are also helping to attend to those who are quarantined. Cart-like automatons are being used to deliver food to people confined to hotel rooms during this crisis. And quadcopter drones are scurrying back and forth, bringing test samples to labs — and also taking photos of anyone violating shelter-in-place restrictions.

These incredibly diverse robots are providing much-needed help. In a global disaster such as this one, we would be remiss to not rely on robots to relieve as many frontline workers as possible. This includes taking care of sanitization, hourly vital measurements, resource delivery, and even a few duties for local police departments.

A Global Effort

Roboticists at the Center for Robot-Assisted Search and Rescue and Texas A&M University researched over 120 ways that robots are helping 21 countries during this pandemic. They concluded that aerial and ground robots are indispensable in managing the COVID-19 crisis.

Medic robots holds a tubes with a stick for scraping PCR and blood test. Abstract diagnostic room, medical equipment: table, test tubes blood samples on blue background. copy space

Besides disinfecting hospital rooms and hallways, robots are also delivering food and prescriptions to patients. Some are even handling the extra paperwork and data entry that comes along with a surge in patients.

Outside of many hospitals, sanitizing robots are hard at work spraying disinfectant in public areas. Besides identifying people violating their local stay-at-home orders, drones are being used to create thermal images to find people with fevers and remind people to stay more than six feet apart. One country is even using a robot to roll through crowds, with audio advising people about the virus and social distancing.

For those who must keep working but cannot work from home from a computer all day, robots are helping them keep up business as usual. At a new hospital in China, construction workers worked through the night as drones carried the lighting around the hospital.

Realtors are using robots to teleconference with video during property tours without leaving their homes. Japanese students “attended” graduation with robots walking the stage. And in Cyprus, Greece, a man used his drone to walk his dog so he wouldn’t have to violate the shelter-in-place restrictions.

A Helping Hand, Not a Replacement

Although some of the robots in use are autonomous, that is, developed with AI, there is zero indication that they are taking over human jobs. Instead of displacing workers, robots are aiding them in a myriad of ways.

Robots work faster than most humans, saving time and effort that can be used more productively elsewhere. Aside from efficiency, the usage of robots also helps save vital personal protective equipment (PPE).

All around the world, robots are undertaking tasks that humans could not do safely during this time. This also prevents workers from being exposed to the virus, which is going to save countless lives.

Off-the-Shelf Instead of Cutting-Edge

It’s important to note that, while many startups and research labs are innovating during this time and developing medical robots, these new robots are highly unlikely to make any sort of impact now. Most medical facilities are choosing to rely on tried-and-tested automatons rather than new prototypes.

Most often, robots used in an emergency, especially those making a major difference, were already in common use before the disaster occurred. One reason is that healthcare workers (and other frontline workers) are way too inundated with smaller details during a disaster to spend any time learning or training on a new machine.

Robots already in use can be repurposed in the event of a disaster: agriculture drones, ones that spray pesticides over many acres of farmland, are being used in India and China to spray disinfectant over large urban areas.

Newer iterations of robots will certainly be useful in future pandemics. Which leads us to our next thought: We mustn’t pause innovation when humanity recovers from this pandemic.

Robots Can Help Us With Future Crises

From Los Angeles to Beijing, COVID-19 has brought the world in a standstill. It’s inspiring to see how robots are being used to help humans handle this pandemic better. We hope that the benefits these automatons have brought spur new innovation in this field. It’s clear that robots hold vast potential to help out in crises like this pandemic.

What developments would you like to see in the field of robotics? Did you hear about any other examples of robots helping out during the COVID-19 crisis? As always, feel free to let us know your thoughts in the comments below!

In fact, lack of cybersecurity in IoT systems is actually hindering the field from reaching its full potential. To make matters worse, security features for IoT applications can vary from developer to developer. It, unfortunately, isn’t uncommon for security to take a backseat in the development process until the end, when there’s no budget or time left to implement a proper solution.

But this approach isn’t cutting it. In IoT applications, it’s not enough to create a set-it-and-forget-it security layer; IoT isn’t like a PC or any previous technology we’ve encountered before. Because there are so many sensors, devices, and a cloud component, it takes a lot of thought and planning to create a robust, secure IoT system.

Could AI help solve this conundrum before it’s too late? The short answer is yes and no. The long answer? Keep reading.

The Bright Side: Helpful AI

Right now, AI development within IoT applications is limited to data analysis, predictive analytics, and generating notifications for a human to take a closer look. It does very well in this area, and there’s still a lot to learn and apply. But even this application results in a lot of false positives for humans to sift through manually. In this case, applying AI to assist with IoT cybersecurity could cause more issues than it helps solve.

Is it possible for AI to train on known patterns of security attacks and breaches? Yes, but we would have to put the IoT system itself through the attacks multiple times so the AI can learn each nuance properly. And when hackers change up their methods and patterns, we’ll have to ensure our AI-enhanced cybersecurity protocols are trained on these changes immediately. Otherwise, this type of AI application can quickly make itself become obsolete and useless.

Whereas a company may have full-time dedicated cybersecurity and IT teams, it would also be imperative that they employ a full-time team of ethical hackers to constantly come up with new ways to breach their company’s security protocols. Even then, it’s not a 100% guarantee that an IoT system is fully secured against any type of hacking attempt.

One major hurdle is the lack of training data available for these breaches; companies who’ve been breached in the past are not likely to openly give out details of how and why their security systems were breached. Because of the nature of the Internet, anyone could use this information maliciously against other companies or against the same company again.

And more importantly, releasing information about a breach implicates personal and sensitive data that could upset customers.

The Dark Side: Malicious AI

AI is what you make it. If you’re developing AI with malicious intent, it can certainly be used to bring down a business’s operations for a few days or leak private data into the open waters of the Internet.

And as hackers get smarter and more creative with the increasing number of tools at their disposal, AI will be used to help breaches successfully occur, rather than help prevent them. This type of AI has already been lovingly named by experts as “enemy-AI”.

Enemy-AI is arguably easier to develop, train (it’ll take any training data it can get because any information can help it facilitate a security breach), and apply to attacks, especially when compared to using AI as a defense mechanism.

When we discussed companies not releasing breach information to the public, we see that with enemy-AI, there is no ethical standpoint. With hackers, we must assume that they have no morals or ethics that they follow; if something can yield losses for a company, we must assume that hackers find that information valuable as a result.

Which Will Take the Cake?

The cynic in us believes the malicious AI can easily win over the defense AI. But there is hope yet because it’s likely that neither “side” will ever truly win. There is a ton of value in using AI for IoT security. As far as what this value is used for depends on who gets to it first and who implements it best.

Ultimately, developers must prioritize security as a main part of IoT systems development. At our Los Angeles-based mobile app development studio, we’re planning for security layers in our applications from Day 1; we believe cybersecurity is so important to connected devices and systems that our own CTO is an ethical hacker!

Although AI has limitless potential for every industry today, we mustn’t expect it to do all of the heavy lifting, especially in the ever-evolving world of cybersecurity. AI is a tool that imperfectly does what we tell it to do, and we mustn’t expect anything more from it — at least for now.

However, for most of us, AI’s biggest impact will be felt from its implementation in our healthcare systems. By providing predictive insights into current operations, AI will allow us to solve challenges with fewer resources and effort — and achieve better results.

Perhaps most astonishingly, we’ll even be able to apply AI to predict mortality rates. In turn, these predictions will save countless lives.

How Can AI Predict Mortality Rates?

Machine learning, a subset of AI, is the main technology used in this highly specific, well-defined problem. Whereas machine learning is focused on constantly learning and adapting to new information, AI more broadly seeks to execute solutions intelligently. AI applications can thus use machine learning or deep learning (more on that later) to improve their accuracy and speed.

In a recent study, a new algorithm using machine learning and AI was found to better predict mortality rates for a group of patients at a higher accuracy than doctors and the already-established algorithms out there.

The algorithm trained on multidimensional health data from the UK Biobank, consisting of more than 500,000 patients from 2006 to 2016. Nearly 14,500 patients died earlier than the national average from heart disease, cancer, and respiratory illnesses in this set of training data.

After the algorithm was trained with this data, it was given the task of computing the probability of each patient’s premature death, specifically from chronic disease. The researchers used two types of AI to test the algorithm’s results: deep learning and random forest.

Testing A Variety of Analysis Methods

The first method that the researchers tested was deep learning. Deep learning is a subset of machine learning. The algorithm tries to replicate how a human would analyze data. In deep learning, the algorithm outputs the outcomes of the scenario it just analyzed, and then it alters the scenario slightly to re-analyze it and see if it improves the outcome.

Random forest is also a subset of machine learning. It uses clusters of decision trees, which allocate data into pre-defined classifications using yes/no to figure out whether they fit or not. In the mortality algorithm, the classification was “is the individual likely to die from chronic illness?” The classifications are then collected from the decision trees, compiled, and analyzed to find the most common classification. The most common classification is the algorithm’s prediction.

After these two types of algorithms were run, researchers compared the results to one of medicine’s most used early mortality predictors: the Cox model.

Different Methods = Different Results

Each of the three methods (deep learning, random forest, and the Cox method) focused on different variables while still maintaining priority for major factors like gender, age, and smoking history. For example, the Cox model focused more on physical activity and ethnicity. The deep learning algorithm focused on alcohol intake, usage of certain medications, and exposure to polluted air and job-related hazards. Meanwhile, the random forest algorithm set its focus on waist size, fruit and veggie servings per day, skin tone, and body fat percent.

Ultimately, the two AI algorithms significantly out-performed the Cox model. While the Cox model predicted 44% of the premature deaths between 2006 and 2016, the deep learning algorithm honed in on 76% of the premature deaths. The random forest algorithm also performed well, coming in at 64% accuracy.

These results are astonishing and point to AI as a strong piece of the healthcare and medicine machine. In the past, AI has been used to analyze PET scans of potential Alzheimer’s patients to predict which patients would eventually be diagnosed. The algorithm predicted the disease’s infiltration with 84% accuracy. In other research studies, AI has performed well in predicting autism, heart attacks, and strokes, and even identifying potential diabetes diagnoses.

A Non-Negotiable Part of a Doctor’s Toolkit

AI is improving every day, and doctors and medical developers alike are placing their bets on AI to save lives by warning patients ahead of time about their risk of premature death. Using this information, patients can adjust their lifestyle, diet and exercise habits, and work to move off of high-risk medications. Physicians can even use AI to help them identify patients even earlier to keep a close eye on.

At our Los Angeles-based mobile app technology studio, we’re constantly seeing new and innovative medical applications being fueled by AI and believe this technology will play an integral role in the future of healthcare. As it continues to evolve, AI will undoubtedly impact every industry and business vertical. But we’re confident in saying that its effects will be felt most deeply in the experiences we each receive as patients.

What will it take to make a trip to Mars viable? Relativity Space thinks it has the answer: Huge 3D printers, massive robotic arms, and, of course, some artificial intelligence (AI) development to tie it all together. If the company is correct, it could reinvent how we build rockets to reach the stars — and return to Earth.

Rethinking Rockets from the Ground Up

Tucked away next to Keanu Reeves’ stunt gym and across the street from one of Snoop Dogg’s recording studios is Relativity Space, a small startup with big ambitions. Founded by 29-year-old CEO Tim Ellis and 26-year-old CTO Jordan Noone, the company is on a mission to do for space rockets what Henry Ford did for the car: optimize their production via automation.

The fact that this rocket-building robotic factory is neighbors with these A-listers’ regular haunts emphasizes one of Relativity’s main selling points — that they can build rockets anywhere, even on Mars — as long as there’s ample space for 3D printers and robots. Behind the loading bay doors of the company’s Los Angeles development headquarters, you’ll find four enormous metal 3D printers producing rocket parts around the clock.

Stargate is the name of Relativity’s latest proprietary printer. 30-feet-tall and equipped with two colossal robotic arms, Stargate printers will be responsible for manufacturing roughly 95 percent (by mass) of the startup’s first rocket, Terran-1. Parts like electronic elements, cables, rubber gaskets, and moving parts account for the remaining 5 percent.

Making a rocket 3D-printable was no small feat; Relativity’s team had to rethink and re-engineer their typical design to make it work. Terran-1 will end up having 100 times fewer parts than a normal rocket. And while a conventional liquid-fueled rocket usually consists of thousands of components, Terran-1’s Aeon engine will be assembled from only 100 parts.

This simplicity in design will pay off in more ways than one. According to Ellis, Relativity will theoretically be able to transform raw materials into a rocket ready for the launch pad in just 60 days. But this won’t be proven true until at least 2021, the earliest expected year for the Terran-1 to lift off.

3D-Printing a New Way to the Stars

When fully assembled, Terran-1 will be about 100 feet tall and capable of carrying 2,800-pound satellites to low Earth orbit. That puts it right in the medium range of payloads; it’s more than small launchers like Rocket Lab’s Electron, but far less than SpaceX’s massive Falcon 9 rocket.

It’s worth mentioning that Relativity Space is far from the only company leveraging 3D printing. Rocket Lab, SpaceX, Blue Origin, and dozens of others are also using the revolutionary technology to produce parts. But Ellis and Noone are doubling down more on 3D printing for good reason; they see it as an essential piece of the puzzle to establishing transportation infrastructure to and from Mars.

Prior to Relativity Space, both Ellis and Noone worked on University of Southern California’s renowned rocketry team. After college, Ellis went to work for Blue Origin while Noone became employed at SpaceX. That’s when Ellis started to envision a different type of rocket factory — one run by robots as much as possible.

Fast forward to now, and the duo is many steps closer to making that vision a reality. They’ve certainly come a long way. the first iteration of Relativity’s Stargate printer is approximately 15 feet tall and has three robotic arms which it uses to monitor printing progress, weld metal, and correct defects. While the newest Stargate model only has two arms, it can print large components like fuel tanks or rocket bodies in a single go. Ellis says the next version of Stargate will be twice as big (around 60-feet-tall) to print even larger parts.

The Brains Behind the Robots

While the Stargate printers are used to take care of larger rocket elements, Relativity relies on commercially available metal 3D printers to produce components requiring more precision, like the engine. To get the fine-tuned nuances they need, Ellis and Noone turn to AI.

Before a print like this, Relativity’s team runs a simulation to see what the ideal print would look like. Then they run the real thing while sensors capture environmental data. By comparing these two outcomes, the team is able to train the machine learning algorithms behind the printer to recognize and correct mistakes until it can produce a flawless iteration of the component.

Ellis believes this process will be integral to perfecting automated manufacturing on other worlds. “To print stuff on Mars you need a system that can adapt to very uncertain conditions,” he explains. “So we’re building an algorithm framework that we think will actually be transferable to printing on other planets.”

Per Aspera Ad Astra

Like any ambitious startup, Relativity Space has its doubters. Max Haot is the CEO of Launcher Space, another rocket startup that utilizes 3D printing. He explains that, while everyone in the aerospace industry is eager to tap into the benefits of 3D printing, it may not be the best production method for every component:

“The question is whether 3D printing aluminum tanks is worth it when compared to the traditional tank manufacturing methods. We don’t think so, but let’s see where they take it.”

Still, that hasn’t stopped Relativity from securing hundreds of millions dollars’ worth of deals with numerous satellite operators. And many of the startup’s investors and advisors see much more potential in the company than just launch services.

Shagun Sachdeva, Senior Analyst at space consultancy Northern Sky Research, says “Even if we don’t get to the point of full rocket manufacturing on Mars, Relativity may be able to manufacture other components in orbit. That’s a pretty big development for the industry as a whole.”

When their first rocket is completely assembled, Relativity’s team will ship it to Kennedy Space Center’s Launch Complex-16 for liftoff. What do you think of Relativity’s out-of-this-world approach to rocket building? Do you think this is the future of space exploration? Let us know your thoughts in the comments!

Alan Turing, the famous mathematician, once posited a very interesting strategy to AI development. He asked, “Instead of trying to produce a program to simulate the adult mind, why not rather try to produce one which simulates the child’s? If this were then subjected to an appropriate course of education, one would obtain the adult brain.”

Is this idea from Turing the secret to producing better AI? Researchers are eager to find out.

Learning as a Child

Even though Turing published this profound thought in his 1950 paper titled “Computing Machinery and Intelligence”, the theory is still valid today. Many AI developers now follow this approach for their AI programs. But although the thought is important and carries vast potential, the result right now is far from the complex and multidimensional brain we possess both as children and as adults.

Turing followed up his original musing with some more insight: “Our hope is that there is so little mechanism in the child brain that something like it can be easily programmed.” Today, we are discovering that it’s easier to program AI to beat the world’s best Go players than it is to mimic a brain, child or not.

One of the biggest reasons for this discrepancy is that AI really excels when it’s assigned to solve niched down problems. Give an AI a multitude of questions or tasks from a variety of areas, and all of a sudden, the AI starts making no sense.

But Berkley professor Alison Gopnik and her research team are convinced that children hold the key to early-stage advanced AI. They study how children learn, how sophisticated their learning approaches are, and how and when children outperform algorithms.

This year’s International Conference on Machine Learning took place in Long Beach, just outside of Los Angeles. The topic of children, youth, and AI was discussed in a talk at the conference. We’ve recapped some of the most interesting research here.

AI’s Intensive Needs

The most obvious difference between four-year-old brains and our current AI algorithms is that children only need a few examples to begin generalizing ideas and thoughts. Conversely, machine learning algorithms require tons of data to start making connections. They’re valuable in their own way: no human could ever sift through that much data in one go, but without this amount of data, the ML algorithm wouldn’t return quality insights or decisions.

Additionally, data getting fed into ML algorithms must be labeled. New approaches don’t require labeling, but it’s the best and fastest way for the algorithm to understand what exactly it needs to understand from each piece of data. It’s similar to learning from a textbook by referencing the answer key for questions. Children learn in this way too, but again, they need just a few examples and thus much less time to reach the same conclusions.

Imagination’s Importance

Children can also quickly apply their conclusions across a variety of use cases once they’ve learned the correct lessons. They don’t need supervision, and they need not be familiar with the situation to realize the same conclusions can be applied.

Show a child two different pictures of flowers, and the next time you go out, the child is likely to point out landscaping and excitedly announce, “Flowers!” However, machine learning algorithms that work to identify images need thousands or millions of images to even start forming their own pattern.

Gopnik says that children have “abstract generative models” (imagination) that allows them to extrapolate additional situations that might yield the same conclusions. Unlike ML algorithms, by anticipating an answer, children are answering the textbook questions and then checking the answer key.

Children understand cause and effect, experience, and consequences. They can use this information to continue inferring and imagining.

AI researchers are already applying this research about generative models to their algorithms. However, says Gopnik, algorithms may never reach the imagination or broad thinking similar to children.

Watch and Learn

Research out of Gopnik’s lab also shows that children have intuitive Bayesian probability reasoning. In Bayes’ theorem, you use information from past events to inform the current probability of an event. For example, if a needle hurt a child’s finger before, when the child encounters a sharp pencil, he or she may realize that the pencil point could likely hurt.

In machine learning, developers often reach a crossroads when resources or time are limited, where they must decide whether they should look for new strategies or just take the simplest path to results. Termed “explore-exploit”, researchers find that children also go through this conundrum, but they usually pick exploration, rather than exploitation. But adults are more likely to exploit their combined knowledge, rather than explore other approaches. As adults, we’re more risk-averse and less creative.

Perhaps it is true that we will never be able to truly create a child-like algorithm that explores, imagines, and extrapolates across a variety of subjects. But that won’t stop researchers and developers from continuing to watch children and apply their actions and learnings to AI algorithms.

It turns out, at the end of the day, that the best way to develop an AI is to simply watch and learn.