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Apple recently announced its plans to add more health detection features in their next series of watches, particularly hypertension and apnea among others.
However, the company’s push towards healthcare dates to 2011 when Apple worked with Avolonte, a health-care company, on a secret project dubbed E5 that aimed at creating non-invasive blood glucose monitoring. They even wanted to start Apple Clinics.
All these, probably to address the broken healthcare system in the US.
Interestingly, Apple isn’t the only tech company that’s tried to get into the healthcare sector. Being considered a broken industry by many tech players, healthcare is also seen as a prospective revenue stream. Microsoft, Google, Amazon, and Oracle, have all entered the health space.
Late last year, Microsoft started utilising GPT-3 to find solutions and impact across the healthcare sector, including consultation and treatment recommendations. Now, they are busy experimenting with GPT-4.
Google, on the other hand, through their large language model Med-PaLM-2 is looking at assisting with medical questions in an accurate and safe manner.
Recently, Oracle unveiled a number of innovative solutions to enhance patient care, clinical expertise and improve the healthcare system as a whole.
Here’s a quick glimpse into what big tech are upto in the healthcare industry:
An “Apple” A Day, Keeps Doctor Away Smiling
“If you zoom out into the future and you look back and you ask the question – what was Apple’s greatest contribution to mankind? It will be about health.” In a 2019 CNBC interview, CEO Tim Cook, spoke about ‘democratising’ healthcare and their aim to empower individuals in managing their health by leveraging existing institutional knowledge, and highlighting their early stages in this endeavour.
While Apple had been aggressively working on integrating health monitoring and disease prevention into its devices, Bloomberg reported that the company’s strategy faced setbacks owing to philosophical differences, conservative culture and technological constraints.
Investing time and millions of dollars, researchers at Avalonte worked on using short-wave infrared absorption spectroscopy as a method for non-invasive glucose measurement, however it did not materialise. This led to the company releasing the Apple watch as a niche accessory to complement an iPhone, and not as a health device, as per original vision.
Healthcare struggles
In a WSJ Podcast, health technology reporter, Rolfe Winkler, called healthcare as technology’s “white whale.” Being a huge, attractive market for disruption, the field is considered complex- “It’s not like you can write software that just solves problems faced by the human body and all the regulations, payer schemes, and just junk that goes with healthcare.” Apple, along with others, experimented in this direction.
In 2021, Haven healthcare, a joint venture between Apple, JP Morgan and Berkshire Hathaway, was dismantled in less than three years of its launch. The collaboration was set up with an aim to lower costs and improve outcomes in employee healthcare. However, the venture failed owing to inadequate market power, existing US healthcare system and the pandemic. The failure marked the difficulty of altering American healthcare, a webbed framework of doctors, insurers, drugmakers and middlemen that costs the country $3.5 trillion annually.
Apple even wanted to experiment with a primary care service for its employees by linking data from their devices with clinical care. However, the project did not pick up as employees questioned the integrity of data collected through the service.
Will Not Give Up
Despite initial setbacks on their health devices, and failed plans of health clinics, Apple is relentlessly working towards adding additional health features on its devices. The company. It is said that next year, Apple watches will be able to detect hypertension and sleep apnea. Furthermore, Apple is also working on bringing hearing-aid capabilities to its AirPods.
The company is also aggressively working on wellness-related features to address mental well-being of users. Earlier this year, it was reported that Apple is working on an AI-powered health coaching service for tracking emotions. Furthermore, there are plans to make Vision Pro headsets as a health and fitness device too.
With big tech companies aggressively entering the healthcare sector, latest being OpenAI that partnered with Whoop to power their smartwatches, Apple seems to be way ahead with a solid roadmap and vision for the health domain. The company is also in talks to get regulatory approvals that can allow Apple to interpret data for its users.
Interestingly, out of the big tech companies that are actively involved in the space, Apple is the only company that has niche hardware health products for direct users. Other tech giants are offering solutions that mostly aid hospitals and clinics in offering better solutions for patients. Apple’s involvement in health results in consumer products that are accessible for everybody – a truly unique approach in healthcare.
Going by its ambitious plan, it is likely that 2024 might be Apple’s year for further health tech breakthroughs. Who knows owning Apple products might just save your life someday.
The post Apple is Finally Making Strides in Healthcare appeared first on Analytics India Magazine.
Regulations on artificial intelligence (AI) do not necessarily inhibit smaller players in the market, where there are opportunities for use cases to be built on top of current major platforms.
Critics of regulatory policies, such as mandatory certification and licensing requirements, have argued that these rules will boost the foothold of large market players while rising entry barriers for startups looking to break into the market.
Also: Companies aren't spending big on AI. Here's why that cautious approach makes sense
Such regulation would crush innovation, said Andrew Ng, Stanford University professor and co-founder of Google Brain, on suggestions that AI could be made safer through mandatory licensing schemes. "There are definitely large tech companies that would rather not have to try to compete with open source [AI], so they're creating fear of AI leading to human extinction," Ng said in a recent interview with Australian Financial Review.
While he noted that the absence of policies is better than instilling bad ones, Ng pointed instead to the importance of having "thoughtful" regulation, such as the need for transparency from tech companies. This action could have helped prevent the harm these companies created with social media and would navigate the industry away from a similar result with AI, he said.
AI regulations, though, do not necessarily inhibit startups and market entrants, said Florian Hoppe, partner and head of vector in Asia-Pacific at Bain & Company, in response to ZDNET's question on the impact of legislation on AI innovation.
Also: 4 ways to detect generative AI hype from reality
Large language models (LLMs), for instance, are costly to build and smaller players typically will lack the resources to develop their own. However, there are opportunities for new use cases to built on top of existing LLMs, such as specialized or domain-specific AI applications and models, Hoppe said.
Startups will be able to develop such products without the constraints of having to build their own LLMs, he noted, adding that regulations play a necessary role in mitigating AI risks.
Conversations have also been healthy between governments and industry players on how the regulatory framework for AI should evolve moving forward, added Sapna Chadha, Google's Southeast Asia vice president. This situation is true for the region, which is technology- and digital-forward, setting the right path ahead for Southeast Asia markets to strike a good balance between the need for regulation and the requirement to drive market innovation, Chadha said.
A conducive environment will be essential to ensure that AI can bring about economic and business benefits, while safeguarding against potential risks, such as data bias, said Fock Wai Hoong, Southeast Asia head for Singapore's state-owned investment firm, Temasek Holdings.
Driving Southeast Asia's digital economy revenue toward $100B
In fact, data infrastructure and regulation are among key enablers that will push the region to become a sustainable digital economy, according to the latest e-Conomy SEA (Southeast Asia) report released by Google, Temasek, and Bain & Company.
Also: AI at the edge: Fast times ahead for 5G and the Internet of Things
Investments in digital and physical infrastructures and economic development plans will better enable digital organizations to expand services to areas outside metro cities in the region, where demand for digital products and services is growing, the report noted. These investments, if done right, can drive digital adoption and reduce the cost to serve.
According to this year's e-Conomy report, the region has weathered global macroeconomic headwinds better than other regions, with GDP growth at above 4% and consumer confidence showing a rebound in the second half of 2023, after falling to lower levels in the first half.
The Southeast Asian digital economy is projected to hit $100 billion in revenue this year, clocking 27% in compound annual growth rate since 2021, and growing 1.7 times as fast as gross merchandise value (GMV). E-commerce, travel, transport, and media will account for $70 billion in revenue, the report estimates.
Also: As developers learn the ins and outs of generative AI, non-developers will follow
GMV is expected to expand 11% year-on-year to $218 billion in 2023, with travel and transport on track to exceed pre-pandemic heights next year.
E-commerce also remains on a growth path this year, increasing 22% in revenue year on year to hit $28 billion. GMV in the sector is projected to climb to $139 billion in 2023, before hitting $186 billion in 2025 on a 16% growth rate.
To further sustain digital growth in the region, the report points to the need for digital businesses to focus on monetization and establish a path to profitability. In addition, digital inclusion remains crucial, and governments in the region must continue to invest in infrastructure building and to plug connectivity gaps in rural areas. This will ensure digital services are accessible in geographies where there is growing consumer demand.
The report also calls for the development and harmonization of policies and agreements, such as trade and data governance agreements, across Asean and to ease cross-border data flow and digital economy activities.
A policy framework for responsible AI development, for instance, can encompass "balanced legal frameworks" for AI innovation, with privacy laws to safeguard personal data and enable trusted cross-border data flows.
Also: Organizations are fighting for the ethical adoption of AI. Here's how you can help
AI governance frameworks should also be interoperable across the region and globally, with the development of common standards and shared best practices to ensure AI technologies are developed and adopted responsibly.
Southeast Asia is showing resilient growth, but the region is fragmented with diverse markets and siloed policy frameworks, Fock said. He urged for focus on establishing unified and multilateral agreements, such as digital economy agreements, and pulling together disparate efforts between the individual markets.
A "single digital Southeast Asian" market, where there is interoperability and seamless connectivity, could be the model to drive growth in the region, he said. Singapore, for instance, already has digital economy agreements with nations such as France, New Zealand, and the UK, and can look to make similar pacts with its peers in Southeast Asia.
Asean member states in September said that they are working to establish protocols that will ease cross-border digital trade and help address emerging trends, such as AI. Targeted to be completed by 2025, the Asean Digital Economic Framework Agreement will improve digital rules across key areas, including digital trade, cybersecurity, payments, and data. The framework aims to facilitate seamless cross-border online trade and make it easier to do business within the region.
Asean has also championed its unified efforts in cybersecurity and pledged to drive further collaboration among member states, including plans to adopt common standards and best practices. To date, Asean is the only regional organization to have subscribed, in principle, to the United Nations' 11 voluntary, non-binding norms of responsible state behavior in cyberspace.
Redis announced that its Redis Cloud service has secured PCI DSS Level 1 certification, claiming to improve its suite of security and privacy assurances. This compliance is essential for all organisations handling cardholder information and involves rigorous checks to safeguard such data through various protective measures.
The team earned this certification through a thorough evaluation by a qualified security assessor. The certification, which covers Redis Cloud’s flexible and annual plans across AWS and Google Cloud, implies that Redis can securely handle over six million transactions a year.
This update comes soon after the real-time database company announced the integration of Redis Enterprise Cloud’s vector database capabilities with Amazon Bedrock, a service designed to facilitate the creation of generative AI applications using foundation models (FMs).
Redis has a suite of premium customers like GitHub, X, Stack Overflow and Craigslist.
A few months ago when ChatGPT experienced downtime due to a flaw in an open-source library, which exposed user conversation titles and ChatGPT Plus subscribers’ payment details, Redis played a crucial role in resolving the issue.
Read more: When Redis Helped ChatGPT Keep the Conversation Going
The post Redis Cloud Achieves Payment Industry’s Highest Data Protection Standard appeared first on Analytics India Magazine.
In a strategic move aimed at expanding its presence in Europe and boosting its global digital capabilities, Infosys has unveiled its latest proximity center in Sofia, Bulgaria today. The IT giant also has a commitment to fostering local talent and nurturing innovation as the company aims to hire, attract, and up-skill 500 new employees over the next four years.
The new state-of-the-art center in Sofia will focus on harnessing the potential of emerging digital technologies, including Infosys Cobalt Cloud Solutions, Infosys Topaz AI and Automation, Data and Insights, IoT, 5G, and software engineering. This initiative aligns with Infosys’ strategy to amplify human potential by tapping into the local talent pool.
Bulgaria’s growing reputation as a hotbed for IT development, thanks to its robust IT infrastructure and a wealth of local IT specialists, makes Sofia the ideal location for Infosys to establish its new center. It is expected to provide an inviting environment for businesses from various sectors, including financial services and retail, to collaborate and drive digital transformation initiatives. The center will serve as a hub for ideation, incubation, creation, and scaling of innovative solutions based on emerging technologies.
The center will serve both global and European customers and play a crucial role in supporting their AI and Cloud-led digital transformation journeys. It will further solidify Infosys’ client relationships in Europe, with a particular focus on the manufacturing, retail, and financial services sectors. The center’s functions will encompass the rapid expansion of teams specializing in digital and analytical capabilities, as well as SAP and cloud solutions.
Milena Stoycheva, Minister of Innovation and Growth for the Bulgarian Government, expressed her enthusiasm, “The opening of the new Centre in Sofia is a testament to Infosys’ commitment to fostering talent in our country. With a commitment to employing a 500 strong workforce over the next four years, we’re excited to see the company contribute to our local economy and bring new skills and opportunities for talent working in the technology sector.”
Kosta Cholakov, Chief Executive Officer of DZI Insurance said, “Infosys is one of our strategic partners on our digital transformation journey and we’re thrilled to see Infosys expand its presence to Bulgaria, moving ever closer to its clients. We look forward to continuing collaborating together driving innovation with next generation technologies throughout our organization, underpinned by the wealth of talent and expertise.”
Dinesh Rao, Executive Vice President and Co-Head of Delivery at Infosys added, ““We are dedicated to continuing to grow our footprint in Europe to bring our capabilities, skills and expertise ever closer to our clients. Bulgaria is renowned for its excellent IT talent, and we’re excited to build an exemplary workforce that meets the demands for next generation skills and solutions, with a focus on catalyzing progress of our client’s AI and cloud first transformation. Bringing together the strength of local talent with our industry-leading expertise and innovation we’re confident the new center will serve as a hub of innovation to help shape digital Europe.”
This strategic move is poised to significantly impact the digital transformation landscape in Europe, with Infosys playing a pivotal role in driving innovation and nurturing local talent in Bulgaria.
The post Infosys Expands to Europe to Accelerate AI appeared first on Analytics India Magazine.
AMD has introduced the Ryzen 5 7545U processor as a part of the Ryzen 7040U family, which is its best selling GPU, featuring the Zen 4c architecture, a variant derived from the Zen 4 design. This new architectural iteration, termed Zen 4c, shares similarities with Zen 4 but focuses on optimising core density, resulting in reduced die space consumption.
The primary benefits of this architecture include accommodating more cores and enhancing power efficiency, as AMD outlines its objectives for Zen 4c.
Zen 4c’s key design characteristics are notable, with a Zen 4 core occupying 3.84mm2 of die space, while a Zen 4c core requires only about 2.48mm2. This reduction in size amounts to approximately 35 percent less area.
Zen 4c utilises half the L3 cache compared to Zen 4, but the primary reason behind its space-saving capability is the lower maximum clock speed. This reduced frequency mitigates interference and power leakage, enabling the chipmaker to optimise the core layout for density.
The increased density of Zen 4c contributes to better latency and heightened power efficiency. AMD aims to retain the same instruction set and IPC as Zen 4 while using less power, ultimately delivering superior performance at power levels below 15W.
Key Specifications of the AMD Ryzen 5 7545U:
Manufactured on TSMC’s 5nm node.
Comprises 2x Zen 4 cores and 4x Zen 4c cores.
Prioritizes Zen 4 cores in the OS scheduler to achieve a higher absolute frequency.
Offers a total of 6 cores and 12 threads.
Supports a maximum clock speed of 4.9GHz and a base clock speed of 3.2GHz.
Operates within a 15-30 TDP (Thermal Design Power) range.
Equipped with a Radeon 740M integrated GPU for graphics processing.
AMD’s strategy involves deploying smaller Zen 4c cores in premium laptops with increased core counts, as well as entry-level laptops, where these cores provide the same IPC but with a more compact design.
As of now, AMD has not disclosed specific details regarding the availability and pricing of the new Ryzen 5 7545U processor.
The post AMD Formally Launches Ryzen 5 7545U Processor with Zen 4c appeared first on Analytics India Magazine.
In 2018, when DeepMind unveiled AlphaFold to the public, a pivotal discovery in medical technology that could predict single-chain protein structures, it disrupted the way biology is done, giving new directions in the field of structural biology.
Two years later, DeepMind announced AlphaFold 2, which cracked the half-century puzzle of protein folding, a defining moment for computational science and AI in the realm of life sciences. Since then, we have had AlphaFold-Multimer, AlphaFill and more.
While the original AlphaFold was pivotal for predicting single-chain protein structures, the newest version, AlphaFold-latest, released this week, is even bigger and better. It can now anticipate structures from nearly all molecules in the Protein Data Bank (PDB)—a comprehensive database for 3D biological molecule structures—and has extended its capabilities to include small molecules, proteins, nucleic acids, and molecules with post-translational modifications.
The AlphaFold-latest has been developed by Isomorphic Labs, a London-based company under Alphabet’s umbrella, along with DeepMind.
Isomorphic Behind AlphaFold-latest Revolution
Founded in 2021 by Demis Hassabis, who also had a significant role in DeepMind’s inception, Isomorphic endeavors to use AI in drug discovery and research on severe human diseases.
The brains behind Isomorphic include tech veteran Miles Congreve, serving as chief scientific officer, who contributed to the design of 20 clinical-stage drugs and co-invented Kisqali (Ribociclib), a marketed breast cancer treatment. Sergei Yakneen is the chief technology officer with over two decades of expertise spanning engineering, machine learning, product development, and research in life sciences and medicine.
The company’s name reflects its philosophy: the idea that strategies from AI can be mapped onto pharmacology to solve complex biological challenges more efficiently.
At the core of Isomorphic Labs’ philosophy is the belief in an interdisciplinary approach, combining insights from AI with biosciences to innovate and accelerate the development of new medicines. They prioritise AI and machine learning, suggesting a transformative potential beyond the mere augmentation of existing methods.
The goal of Isomorphic Labs is to make the drug discovery process more scalable, reducing the traditionally long timelines and high costs of bringing new treatments to market.
As they continue to navigate the complex landscape of AI-driven drug discovery, Isomorphic Labs’ philosophy is likely to evolve, adapting to new challenges and scientific advancements in their pursuit of revolutionising how we discover and develop new medicines.
What AlphaFold-latest will Achieve
The new AlphaFold model surpasses traditional methods like AutoDock Vina in ligand docking accuracy by starting from scratch with only protein sequences and ligands. It also betters its predecessor, AlphaFold 2.3, especially in predicting protein-protein interactions and excels in modelling antibody binding.
Additionally, it leads in protein-nucleic acid interface prediction and RNA structure forecasting, albeit slightly behind the best manual methods from CASP15. Moreover, it now predicts structures of complex components, including bonded ligands and various molecular modifications.
By accurately modelling proteins, ligands, nucleic acids, and post-translational modifications together, it facilitates a deeper understanding of complex biological mechanisms. A prime illustration is the structure of CasLambda—a variant in the CRISPR system, known for genome editing—bound to crRNA and DNA. The model’s prediction of CasLambda, notable for its compact size, hints at potential for more efficient genome editing applications.
AlphaFold Through the Years
When OpenAI debuted ChatGPT, it broke the internet. However, OpenAI owes it to Google Brain, the brain behind Transformer, the neural network architecture that powers ChatGPT’s language models GPT 3.5, GPT-4 and more, which was introduced in a seminal 2017 paper.
The AlphaFold model is also built with transformers. Transformers have taken the world of machine learning by storm since being introduced by Google Brain researchers in a seminal 2017 paper. The AlphaFold team created a new type of transformer designed specifically to work with three-dimensional structures, which they call Invariant Point Attention (IPA).
Since then, AlphaFold has found a variety of real-life applications including finding vaccines for malaria, liver cancer, COVID-19, delivering gene therapy and more. Not just drug discovery, AlphaFold finds a range of applications beyond it.
For example, DeepMind partnered with the Centre for Enzyme Innovation at the University of Portsmouth to engineer faster enzymes for recycling some of the world’s most polluting single-use plastics. It has been used to design new types of proteins that more efficiently break down plastic waste. The researchers have engineered an enzyme that can break down entire plastic containers. Enzymatic depolymerization is a promising method for recycling plastics, as enzymes can be much more specific than chemical catalysts and can degrade a much more diverse waste stream.
With applications reaching far beyond medicine, AlphaFold has captured the global scientific community’s attention as the most-read paper of 2022. At a time when big techs like Meta is laying off their protein folding team, Google open-sources the codes of AlphaFold, allowing countless companies to innovate and devise extraordinary solutions with the potential to transform human lives.
The post Will Alphabet Achieve With Isomorphic Labs What It Couldn’t With DeepMind? appeared first on Analytics India Magazine.
For the first time, Apple head Tim Cook has peppered his narrative with the term ‘generative AI,‘ stepping aside from the usual ‘machine learning‘ lingo that is pretty common in all Apple conferences.
Against the backdrop of this linguistic jump, Apple has reported a revenue of $89.5 billion for the September quarter, clinching all-time highs in India and setting records in an array of countries from Brazil to Vietnam. However, this quarter is not without its shadows as it marks the fourth consecutive period of a revenue drop, with a year-over-year decrease of 1%.
In the broader context of generative AI, Apple regarded these technologies as the bedrock for the vast majority of their products. “This was exemplified with the launch of iOS 17, which featured innovations such as Personal Voice and Live Voicemail, all underpinned by AI,” said Tim Cook, CEO, of Apple, during the Q3 earnings call.
“Regarding generative AI, we have ongoing projects. I won’t delve into specifics, as our policy is to keep development details confidential but rest assured we are heavily invested in this area. We are committed to responsible innovation, and you’ll see our products progressively integrate these technologies at their core,” Cook added.
Moreover, Apple’s foray into AI is not merely for consumer convenience but also extends to safety-critical applications. Features such as fall detection, crash detection, and the ECG functionality on the Apple Watch, while not overtly marketed as AI-powered, are indeed built upon a bedrock of AI and machine learning.
Parallel to AI ventures, the tech giant’s iPhone revenue surpassed expectations, marking a record for the September quarter and attaining quarterly records in several markets such as China Mainland, Latin America, the Middle East, South Asia, and an unprecedented all-time high in India.
“Despite facing a turbulent macroeconomic climate marked by significant foreign exchange challenges, we have maintained its course by investing in the future and adopting a long-term management perspective, staying true to the principles that have historically steered our success,” said Cook.
The company has recently broadened its retail footprint by inaugurating its first stores in India and opening additional outlets in Korea, China, and the UK. Apple also expanded its online store services to Vietnam and Chile and is on the verge of opening yet another store in China.
Apple is nearing $10 billion in revenue in India. “India represents a vibrant and rapidly expanding market where we have recorded strong double-digit growth and achieved an all-time revenue record,” Cook added.
With a relatively low market share in this large market, Apple sees significant headroom for growth. While the average selling price (ASP) in India may be lower compared to the global average, Apple does not view this as a deterrent. The company regards each market’s trajectory as unique and resists drawing direct comparisons to other markets, such as China’s growth patterns a decade earlier.
The expansion of the middle class and improvements in distribution channels are among the positive indicators Apple has identified in India. The company’s two new retail stores in India have performed better than anticipated, and although it is the beginning of their journey, they are off to a strong start, which aligns with Apple’s overall satisfaction with its current trajectory in the region.
In addressing queries about supply chain priorities, Apple acknowledges the importance of diversification in its supply chain strategies. By continually assessing and adjusting its supply chain, Apple aims to maintain efficiency and adaptability in its operations worldwide.
Read more: Apple Hates AI So Much That It…
The post Apple Uses “Generative AI” in its Q3 Revenue appeared first on Analytics India Magazine.
Back in 2015, information channels were awash with reports of the human attention span having plummeted to 8.25 seconds for a basic task. Even more ignominious was one detail in the report: The human ability to focus registered lower than a goldfish's attention span, which came in at 9 seconds.
You could almost hear an audible sigh of relief when it turned out that none of this was true. (Goldfish apparently do have memories and can learn!)
The seed of this very believable myth was apparently sowed in a Microsoft report. And yet, if you strip away the sensational piece of information that wormed itself into innumerable news reports, the underlying facts have turned out to be alarmingly on point.
Our attention spans are shrinking.
Primed to forget
Dr. Gloria Mark, a professor of informatics at the University of California, Irvine, who studies how digital media affects our lives, has written a book validating the supposition that our ability to focus is in peril.
"In 2004, we measured the average attention on a screen to be two and a half minutes," Mark writes. "Some years later, we found attention spans to be about 75 seconds. Now we find people can only pay attention to one screen for an average of 47 seconds," she says.
From 150 seconds to a 47-second average attention span in just 10 years is a stunning decline that could have profound effects on our species.
Also: Generative AI will far surpass what ChatGPT can do
Many scientists point to a condition called cognitive decline that is taking place among us humans as we become more and more reliant on technology to do things that we used to use our brains to do for ourselves.
When you stop your brain from doing difficult things like reading maps to get to your destination versus using a GPS like Google Maps to guide you there, you are putting the brakes on the very things that helped your species evolve.
It becomes a vicious feedback loop. "Once you stop using your memory it will get worse, which makes you use your devices even more," says Professor Oliver Hardt, who studies the neurobiology of memory and forgetting at McGill University in Montreal.
"We can predict that prolonged use of GPS likely will reduce grey matter density in the hippocampus….GPS-based navigational systems don't require you to form a complex geographic map. Instead, they just tell you orientations, like 'turn left at next light,'" says Hardt.
Also: The best Bluetooth trackers you can buy
Listening to a map app does not engage the hippocampus very much, unlike being forced to engage with a physical map, which requires a much bigger and more complex cognitive process. And it is having disastrous consequences for us.
Hardt points to the brain mapping of people who have been using GPS for a very long time as conclusive proof — they show distinct impairments in spatial memory abilities that require the hippocampus.
It's not altogether surprising, then, that the average American spends 2.5 days a year looking for things, and costing themselves $2.7 billion in replacement costs. Out of this cohort, millennials are slated to be twice as likely as boomers to misplace their things.
Many other measured indications have already signaled something terrible at work; for example, the average person picks up their phone more than 1,500 times per week. In fact, the mere presence of a smartphone decreases cognitive ability.
Hardt doesn't have the data yet, but he believes that "the cost of this might be an enormous increase in dementia."
Memory reboot
University of Waterloo's solution for finding lost things involves outfitting a Fetch robot with an AI algorithm and a camera
What does this all mean for our society? We must prepare for a future in which we're unable to do the most basic things — whether we have dementia or not. In other words, we will have to find a way to survive and exist in an era of potentially chronic memory impairment.
Within that context are two remarkably ingenious and relatively cheap solutions that have been engineered to navigate the world of the memory-impaired.
A research team from the University of Waterloo in Ontario, Canada has designed a companion robot with an episodic memory of its own that, while initially made with dementia patients in mind, is being lauded for its ability to help anyone who is habitually forgetful.
Also: How horses can inform the future of robot-human interaction
If our current problems with memory become any worse, such companion robots could become as prominent a fixture in homes as coffee machines or Roomba vacuums.
The Waterloo research team started off with a Fetch mobile manipulator robot, which sports a built-in camera and a convolutional neural network. Using deep learning and an object-detection algorithm, researchers taught the robot to identify, track, and keep a memory log of objects in the room. This was being stored in video with the ability to record the time and date that objects entered or left the robot's field of vision.
Researchers also developed a graphical interface to enable users to choose objects they want to be tracked.
In other words, if you forgot where you last left your phone in your living room, you would simply type the missing object's name in that interface, and the object's location would be revealed to you.
Also: How to find out if an AirTag is tracking you
Lead researcher Ali Ayub, a post-doctoral fellow in electrical and computer engineering at the university, says that the tracking accuracy is currently around 90 percent but improving.
"The long-term impact of this is really exciting," said Dr. Ayub. "A user can be involved not just with a companion robot but a personalized companion robot that can give them more independence."
Tags with memories
MIT's solution for lost things uses a robotic arm outfitted with an RFID wireless antenna that can detect objects with RFID tags pasted onto them
Researchers at MIT grappled with the same problem of impaired memory but employed a different approach. They used an RFusion robotic arm with a built-in camera and antenna that work together to spot and extricate items that could be completely hidden from view by a pile of stuff.
The thing that makes this solution equally attractive is its simplicity — items have RFID tags — which are batteryless and dirt cheap — pasted onto them. (Radio Frequency Identification — aka RFID — is essentially a wireless system made up of both tags that can emit signals, and readers that have antennas that can receive and interpret them.)
Also: This retailer is using RFID tags to make in-person clothes shopping less frustrating
No matter where the objects are and how many things are piled over them, MIT's RFusion system can haul them out (as long as they are within the zone that the robot arm operates in).
Using machine learning, the robotic arm — which is integrated with the camera and antenna — hones in on the location.
Once it is able to target the right space, the neural network kicks in by meshing information from the RF tags as well as AI-driven visual cues from the camera to clear the area of other objects that may lie on top of the one it is searching for.
The key process here is one called reinforcement learning — where the algorithm is taught to get the robotic arm to its destination in as few moves as possible through a computational reward system.
After adjusting the gripper's angle and width, and verifying the accuracy of the RF tag one last time, the robotic arm picks up the object and places it to one side.
Also: Forget a smartwatch: These are the best smart rings
It's not only the memory-impaired universe that could benefit from this elegant solution.
Thanks to AI algorithms and RFID tags, a whole new cost-effective solution emerges that could navigate piles of orders in warehouses and fulfill them, or work in any industrial setup that requires navigating supply chain complexities.
"This idea of being able to find items in a chaotic world is an open problem that we've been working on for a few years," said Fadel Adib, associate professor in the Department of Electrical Engineering and Computer Science at MIT.
"Having robots that are able to search for things under a pile is a growing need in industry today," he added.
There's been an explosion in the number of artificial intelligence (AI) tools following last year's launch of ChatGPT. Now, Chatbots dominate the media limelight, AI projects number in the hundreds of thousands, Microsoft and Google have joined OpenAI as big names pursuing AI initiatives, and even TikTok, Adobe, and Shopify are incorporating generative AI into their systems.
Although it may seem like generative AI arrived recently, Andy Watson, director of product management and IoT lead at Rightpoint, says that isn't necessarily the case: "There have been folks working on this for decades, but, in terms of the everyday experience and interactions with it, it's a drastically new and powerful technology."
Yet for many of us, AI's greatest impact will reach us right where we live: in our homes. Through automations, virtual assistants, and machine-learning algorithms, AI can make homes more efficient and give you back valuable time.
Of course, AI technology is no newcomer to today's smart home. Whenever you talk to your smart speaker or activate your home security system, you use a resource developed with deep learning in its stack. And while we're a long way from the fully automated, futuristic vision of homes portrayed in "The Jetsons" cartoon show, our smart home journey can start with baby steps — adding an Echo Speaker, Apple HomePod, Google Nest, or even a Samsung TV, installing a smart lightbulb or plug that you can control remotely, or using a security camera you can view on your phone.
However, the reach of AI in the smart home has the potential to go much deeper. Increased automation could enhance our personal lives and boost our productivity. Let's take a look.
AI runs deep in smart home tech
"Bringing the smarts into your entire home is a huge, huge benefit that generative AI can provide," Watson explains.
These benefits can extend beyond today's smart devices, for example, into intelligently enhancing security footage in real time, especially in low-light situations, or generating routines and automations, according to learned patterns and then giving a homeowner the option to confirm them. There's also the opportunity for generative AI to provide predictive maintenance.
AI can learn from your home, your appliances, and your consumption, making inferences from data that, Watson says, "help you identify items in your home that need specific preventative maintenance, either done by the homeowner or to be scheduled through the generative AI on your behalf with a trusted third party."
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Just look at what Google Assistant can now do for Pixel smartphone owners. The virtual voice assistant can screen calls for users, speaking to the caller and notifying the user of what they're calling about, or simply declining to put them on the phone if it's a spam call. Generative AI tools in the smart home could evolve further to set up appointments for your HVAC maintenance, landscaping, or gutter cleaning, which would be based on your specific schedule and availability, using data gathered from the devices.
"If you have a smart hot-water heater that can connect to the internet and gather some data around it, the AI generator would be able to make inferences off of your usage," says Watson, and determine when maintenance would need to be performed.
Living in our fixer-upper house — which has required a lot of intervention from different tradespeople — I can imagine a future where I ditch my iPhone Reminders app in favor of a generative AI assistant.
This assistant could make an appointment with the maintenance company directly, provide me with trusted resources that my neighbors have highly rated, or it could give me personalized recommendations, based on the likes and dislikes it has learned about me. In short, generative AI could soon reduce the many decisions you have to make to just a couple.
What about ChatGPT and voice assistants?
The Amazon Alexa voice assistant uses natural language processing technology and is available in devices like the Echo Show 8.
Generative AI might help make the home of the future smarter, but the impact of AI can already be seen and heard. Amazon, Google, and Apple have been using natural language processing (NLP) systems to create the voice assistants we have at home, including Alexa, Google Assistant, and Siri — and like Google Assistant, Alexa will also get a generative AI upgrade in the coming months.
Watson believes generative AI will turbo-charge these voice assistants: "Voice assistants today… People use them for very transactional things, 'I need something, and you give me something; turn on the light, get the weather,'" he explains. "But from the generative AI perspective, something like ChatGPT has the potential to turn that relationship from transactional to collaborative."
Instead of simple automations that need to be manually configured by the user, generative AI could instead suggest new, more intricate ways that a smart home can work based on what it knows about the user's behavior. This could include turning on a fall-detection system when a specific family member enters the room, or running customized automations when a family member is identified on a security camera.
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This evolution is already evident in the ways that a smart HVAC system can use generative AI to optimize heating, air conditioning, and ventilation. The system combines data from user patterns and environmental factors, such as outside temperature and humidity.
"The real power that we see behind generative AI models lies in their ability to make inferences of immense datasets," Watson says, further explaining that all smart home products generate a vast amount of data rarely used in concert with one another.
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Voice assistants, such as Alexa and Siri, gather user data to learn more about preferences and patterns. Your iPhone uses machine learning to recognize which speaker you typically play music on while cooking dinner, and starts making suggestions when it comes to that time of day, for example.
But this preference data could be used for much more. Watson explains that if generative AI models can infer answers from the user-behavior data they collect, they could make our homes smarter without needing manual intervention and foresight.
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One company, Josh.AI, is leveraging this potential to add the power of generative AI to the smart home. The company officially launched JoshGPT, a voice assistant powered by OpenAI's GPT technology, that can go further than Alexa and Siri right now, thanks to generative AI. Beyond asking Alexa to give a weather report, Josh.AI users can ask JoshGPT to check the weather, turn on a light, and play music in one single request.
JoshGPT can generate answers to your queries, rather than just search for them online or repeat what websites say. You can ask JoshGPT questions much like ChatGPT, but through a voice conversation using microphones and speakers around your home.
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And like JoshGPT, our smart home voice assistants could become smarter than ever and able to handle multiple tasks at a time, such as three questions in a row. "Generative AI can do the thinking for you, if you will, so that anyone can have a smart home," says Watson.
This is where Matter comes in, sort of
A home automated system is controlled from a dashboard.
The potential of generative AI in the smart home comes at a time of significant advancement in connectivity standards.
One of the most important step changes in connectivity is Matter, an open-source standard developed with the influence and investment of big players, such as Google, Apple, and more. Matter brings seamless interoperability between smart home devices that weren't previously capable of communicating with each other.
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Since Matter was launched in 2022, many smart home manufacturers have begun adopting the standard to make interoperability a given in their products.
Watson agrees that Matter makes it possible to have "interoperability between a slew of different devices across different verticals, and multi-admin support for essentially-agnostic voice assistants." Multi-admin is at the center of Matter's purpose, as it means different users can control all their Matter-enabled devices without choosing a single platform.
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"With the consolidation of user experience and the interoperability between these different devices, we now have the potential for a generative AI model to have access to an immense dataset it never had before, to help personalize the smart home experience to the individual homeowner," Watson adds.
What about ethics?
Our homes are our havens of privacy, so the idea of an AI model using data gathered from devices placed throughout a residence won't appeal to everyone. For all the convenience and benefits of having more AI at home, the potential risks mean some could balk.
"AI is a very broad technology. It's fast-moving. I think that's why many people are concerned about the trajectory of where it could take us," Watson says.
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He believes AI requires thoughtfulness, morality, ethics, and some regulations to ensure safety and effectiveness. "In almost everything that we do when it comes to social media, any product that you're not necessarily paying for, you are the product in that equation," he adds.
Watson says consumers will have a choice about whether to trade convenience for privacy. Will they, for example, be willing to share data about their behaviors with other companies to aid the creation of lower-cost products and services?
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"Transparency is step one," says Watson. "People need to know how their data is being used, whether it is being stored, where it's being [sent]."
Different consumers will have different opinions, as with any technology. For now, I'll continue to dream of a world where generative AI can schedule home-maintenance appointments without me having to do anything, set smart home devices that are tailored to my behavior, and where Alexa and Siri can finally understand what I'm asking them — at least 80% of the time.
