In their paper entitled English Broadcast News Speech Recognition by Humans and Machines, the team proposes to identify techniques that close the gap between automatic speech recognition (ASR) and human performance.
IBM’s initial work in the voice recognition space was done as part of the U.S. government’s Defense Advanced Research Projects Agency (DARPA) Effective Affordable Reusable Speech-to-Text (EARS) program, which led to significant advances in speech recognition technology. The EARS program produced about 140 hours of supervised BN training data and around 9,000 hours of very lightly supervised training data from closed captions from television shows. By contrast, EARS produced around 2,000 hours of highly supervised, human-transcribed training data for conversational telephone speech (CTS).
Because so much training data is available for CTS, the team from IBM and Appen endeavored to apply similar speech recognition strategies to BN to see how well those techniques translate across applications. To understand the challenge the team faced, it’s important to call out some important differences between the two speech styles:
Broadcast news (BN)
Conversational telephone speech (CTS)
The team adapted the speech recognition systems that were so successfully used for the EARS CTS research: Multiple long short-term memory (LSTM) and ResNet acoustic models trained on a range of acoustic features, along with word and character LSTMs and convolutional WaveNet-style language models. This strategy had produced results between 5.1% and 9.9% accuracy for CTS in a previous study, specifically the HUB5 2000 English Evaluation conducted by the Linguistic Data Consortium (LDC). The team tested a simplified version of this approach on the BN data set, which wasn’t human-annotated, but rather created using closed captions.
Instead of adding all the available training data, the team carefully selected a reliable subset, then trained LSTM and residual network-based acoustic models with a combination of n-gram and neural network language models on that subset. In addition to automatic speech recognition testing, the team benchmarked the automatic system against an Appen-produced high-quality human transcription. The primary language model training text for all these models consisted of a total of 350 million words from different publicly available sources suitable for broadcast news.
In the first set of experiments the team separately tested the LSTM and ResNet models in conjunction with the n-gram and FF-NNLM before combining scores from the two acoustic models in comparison with the results obtained on the older CTS evaluation. Unlike results observed on original CTS testing, no significant reduction in the word error rate (WER) was achieved after scores from both the LSTM and ResNet models were combined. The LSTM model with an n-gram LM individually performs quite well and its results further improve with the addition of the FF-NNLM.
For the second set of experiments, word lattices were generated after decoding with the LSTM+ResNet+n-gram+FF-NNLM model. The team generated n-best lists from these lattices and rescored them with the LSTM1-LM. LSTM2-LM was also used to rescore word lattices independently. Significant WER gains were observed after using the LSTM LMs. This led the researchers to hypothesize that the secondary fine-tuning with BN-specific data is what allows LSTM2-LM to perform better than LSTM1-LM.
Our ASR results have clearly improved state-of-the-art performance, and significant progress has been made compared to systems developed over the last decade. When compared to the human performance results, the absolute ASR WER is about 3% worse. Although the machine and human error rates are comparable, the ASR system has much higher substitution and deletion error rates.
Looking at the different error types and rates, the research produced interesting takeaways:
The experiments show that speech ASR techniques can be transferred across domains to provide highly accurate transcriptions. For both acoustic and language modeling, the LSTM- and ResNet-based models proved effective and human evaluation experiments kept us honest. That said, while our methods keep improving, there is still a gap to close between human and machine performance, demonstrating a continued need for research on automatic transcription for broadcast news.
Source : https://appen.com/blog/improving-the-accuracy-of-automatic-speech-recognition-models-for-broadcast-news/
The forthcoming wave of Web 3.0 goes far beyond the initial use case of cryptocurrencies. Through the richness of interactions now possible and the global scope of counter-parties available, Web 3.0 will cryptographically connect data from individuals, corporations and machines, with efficient machine learning algorithms, leading to the rise of fundamentally new markets and associated business models.
The future impact of Web 3.0 makes undeniable sense, but the question remains, which business models will crack the code to provide lasting and sustainable value in today’s economy?
We will dive into native business models that have been and will be enabled by Web 3.0, while first briefly touching upon the quick-forgotten but often arduous journeys leading to the unexpected & unpredictable successful business models that emerged in Web 2.0.
To set the scene anecdotally for Web 2.0’s business model discovery process, let us not forget the journey that Google went through from their launch in 1998 to 2002 before going public in 2004:
After struggling for 4 years, a single small modification to their business model launched Google into orbit to become one of the worlds most valuable companies.
The earliest iterations of online content merely involved the digitisation of existing newspapers and phone books … and yet, we’ve now seen Roma (Alfonso Cuarón) receive 10 Academy Awards Nominations for a movie distributed via the subscription streaming giant Netflix.
Amazon started as an online bookstore that nobody believed could become profitable … and yet, it is now the behemoth of marketplaces covering anything from gardening equipment to healthy food to cloud infrastructure.
Open source software development started off with hobbyists and an idealist view that software should be a freely-accessible common good … and yet, the entire internet runs on open source software today, creating $400b of economic value a year and Github was acquired by Microsoft for $7.5b while Red Hat makes $3.4b in yearly revenues providing services for Linux.
In the early days of Web 2.0, it might have been inconceivable that after massively spending on proprietary infrastructure one could deliver business software via a browser and become economically viable … and yet, today the large majority of B2B businesses run on SaaS models.
It was hard to believe that anyone would be willing to climb into a stranger’s car or rent out their couch to travellers … and yet, Uber and AirBnB have become the largest taxi operator and accommodation providers in the world, without owning any cars or properties.
While Google and Facebook might have gone into hyper-growth early on, they didn’t have a clear plan for revenue generation for the first half of their existence … and yet, the advertising model turned out to fit them almost too well, and they now generate 58% of the global digital advertising revenues ($111B in 2018) which has become the dominant business model of Web 2.0.
Taking a look at Web 3.0 over the past 10 years, initial business models tend not to be repeatable or scalable, or simply try to replicate Web 2.0 models. We are convinced that while there is some scepticism about their viability, the continuous experimentation by some of the smartest builders will lead to incredibly valuable models being built over the coming years.
By exploring both the more established and the more experimental Web 3.0 business models, we aim to understand how some of them will accrue value over the coming years.
Bitcoin came first. Proof of Work coupled with Nakamoto Consensus created the first Byzantine Fault Tolerant & fully open peer to peer network. Its intrinsic business model relies on its native asset: BTC — a provable scarce digital token paid out to miners as block rewards. Others, including Ethereum, Monero and ZCash, have followed down this path, issuing ETH, XMR and ZEC.
These native assets are necessary for the functioning of the network and derive their value from the security they provide: by providing a high enough incentive for honest miners to provide hashing power, the cost for malicious actors to perform an attack grows alongside the price of the native asset, and in turn, the added security drives further demand for the currency, further increasing its price and value. The value accrued in these native assets has been analysed & quantified at length.
Some of the earliest companies that formed around crypto networks had a single mission: make their respective networks more successful & valuable. Their resultant business model can be condensed to “increase their native asset treasury; build the ecosystem”. Blockstream, acting as one of the largest maintainers of Bitcoin Core, relies on creating value from its balance sheet of BTC. Equally, ConsenSys has grown to a thousand employees building critical infrastructure for the Ethereum ecosystem, with the purpose of increasing the value of the ETH it holds.
While this perfectly aligns the companies with the networks, the model is hard to replicate beyond the first handful of companies: amassing a meaningful enough balance of native assets becomes impossible after a while … and the blood, toil, tears and sweat of launching & sustaining a company cannot be justified without a large enough stake for exponential returns. As an illustration, it wouldn’t be rational for any business other than a central bank — i.e. a US remittance provider — to base their business purely on holding large sums of USD while working on making the US economy more successful.
The subsequent generation of business models focused on building the financial infrastructure for these native assets: exchanges, custodians & derivatives providers. They were all built with a simple business objective — providing services for users interested in speculating on these volatile assets. While the likes of Coinbase, Bitstamp & Bitmex have grown into billion-dollar companies, they do not have a fully monopolistic nature: they provide convenience & enhance the value of their underlying networks. The open & permissionless nature of the underlying networks makes it impossible for companies to lock in a monopolistic position by virtue of providing “exclusive access”, but their liquidity and brands provide defensible moats over time.
With The Rise of the Token Sale, a new wave of projects in the blockchain space based their business models on payment tokens within networks: often creating two sided marketplaces, and enforcing the use of a native token for any payments made. The assumptions are that as the network’s economy would grow, the demand for the limited native payment token would increase, which would lead to an increase in value of the token. While the value accrual of such a token model is debated, the increased friction for the user is clear — what could have been paid in ETH or DAI, now requires additional exchanges on both sides of a transaction. While this model was widely used during the 2017 token mania, its friction-inducing characteristics have rapidly removed it from the forefront of development over the past 9 months.
Revenue generating communities, companies and projects with a token might not always be able to pass the profits on to the token holders in a direct manner. A model that garnered a lot of interest as one of the characteristics of the Binance (BNB) and MakerDAO (MKR) tokens was the idea of buybacks / token burns. As revenues flow into the project (from trading fees for Binance and from stability fees for MakerDAO), native tokens are bought back from the public market and burned, resulting in a decrease of the supply of tokens, which should lead to an increase in price. It’s worth exploring Arjun Balaji’s evaluation (The Block), in which he argues the Binance token burning mechanism doesn’t actually result in the equivalent of an equity buyback: as there are no dividends paid out at all, the “earning per token” remains at $0.
One of the business models for crypto-networks that we are seeing ‘hold water’ is the work token: a model that focuses exclusively on the revenue generating supply side of a network in order to reduce friction for users. Some good examples include Augur’s REP and Keep Network’s KEEP tokens. A work token model operates similarly to classic taxi medallions, as it requires service providers to stake / bond a certain amount of native tokens in exchange for the right to provide profitable work to the network. One of the most powerful aspects of the work token model is the ability to incentivise actors with both carrot (rewards for the work) & stick (stake that can be slashed). Beyond providing security to the network by incentivising the service providers to execute honest work (as they have locked skin in the game denominated in the work token), they can also be evaluated by predictable future cash-flows to the collective of service providers (we have previously explored the benefits and valuation methods for such tokens in this blog). In brief, such tokens should be valued based of the future expected cash flows attributable to all the service providers in the network, which can be modelled out based on assumptions on pricing and usage of the network.
A wide array of other models are being explored and worth touching upon:
With this wealth of new business models arising and being explored, it becomes clear that while there is still room for traditional venture capital, the role of the investor and of capital itself is evolving. The capital itself morphs into a native asset within the network which has a specific role to fulfil. From passive network participation to bootstrap networks post financial investment (e.g. computational work or liquidity provision) to direct injections of subjective work into the networks (e.g. governance or CDP risk evaluation), investors will have to reposition themselves for this new organisational mode driven by trust minimised decentralised networks.
When looking back, we realise Web 1.0 & Web 2.0 took exhaustive experimentation to find the appropriate business models, which have created the tech titans of today. We are not ignoring the fact that Web 3.0 will have to go on an equally arduous journey of iterations, but once we find adequate business models, they will be incredibly powerful: in trust minimised settings, both individuals and enterprises will be enabled to interact on a whole new scale without relying on rent-seeking intermediaries.
Today we see 1000s of incredibly talented teams pushing forward implementations of some of these models or discovering completely new viable business models. As the models might not fit the traditional frameworks, investors might have to adapt by taking on new roles and provide work and capital (a journey we have already started at Fabric Ventures), but as long as we can see predictable and rational value accrual, it makes sense to double down, as every day the execution risk is getting smaller and smaller
Source : https://medium.com/fabric-ventures/which-new-business-models-will-be-unleashed-by-web-3-0-4e67c17dbd10
In 1776, Adam Smith released his magnum opus, An Inquiry into the Nature and Causes of the Wealth of Nations, in which he outlined his fundamental economic theories. Front and center in the book — in fact in Book 1, Chapter 1 — is his realization of the productivity improvements made possible through the “Division of Labour”:
It is the great multiplication of the production of all the different arts, in consequence of the division of labour, which occasions, in a well-governed society, that universal opulence which extends itself to the lowest ranks of the people. Every workman has a great quantity of his own work to dispose of beyond what he himself has occasion for; and every other workman being exactly in the same situation, he is enabled to exchange a great quantity of his own goods for a great quantity, or, what comes to the same thing, for the price of a great quantity of theirs. He supplies them abundantly with what they have occasion for, and they accommodate him as amply with what he has occasion for, and a general plenty diffuses itself through all the different ranks of society.
Smith identified that when men and women specialize their skills, and also importantly “trade” with one another, the end result is a rise in productivity and standard of living for everyone. In 1817, David Ricardo published On the Principles of Political Economy and Taxation where he expanded upon Smith’s work in developing the theory of Comparative Advantage. What Ricardo proved mathematically, is that if one country has simply a comparative advantage (not even an absolute one), it still is in everyone’s best interest to embrace specialization and free trade. In the end, everyone ends up in a better place.
There are two key requirements for these mechanisms to take force. First and foremost, you need free and open trade. It is quite bizarre to see modern day politicians throw caution to the wind and ignore these fundamental tenants of economic science. Time and time again, the fact patterns show that when countries open borders and freely trade, the end result is increased economic prosperity. The second, and less discussed, requirement is for the two parties that should trade to be aware of one another’s goods or services. Unfortunately, either information asymmetry or physical distances and the resulting distribution costs can both cut against the economic advantages that would otherwise arise for all.
Fortunately, the rise of the Internet, and specifically Internet marketplace models, act as accelerants to the productivity benefits of the division of labour AND comparative advantage by reducing information asymmetry and increasing the likelihood of a perfect match with regard to the exchange of goods or services. In his 2005 book, The World Is Flat, Thomas Friedman recognizes that the Internet has the ability to create a “level playing field” for all participants, and one where geographic distances become less relevant. The core reason that Internet marketplaces are so powerful is because in connecting economic traders that would otherwise not be connected, they unlock economic wealth that otherwise would not exist. In other words, they literally create “money out of nowhere.”
Any discussion of Internet marketplaces begins with the first quintessential marketplace, ebay(*). Pierre Omidyarfounded AuctionWeb in September of 1995, and its rise to fame is legendary. What started as a web site to trade laser pointers and Beanie Babies (the Pez dispenser start is quite literally a legend), today enables transactions of approximately $100B per year. Over its twenty-plus year lifetime, just over one trillion dollars in goods have traded hands across eBay’s servers. These transactions, and the profits realized by the sellers, were truly “unlocked” by eBay’s matching and auction services.
In 1999, Jack Ma created Alibaba, a Chinese-based B2B marketplace for connecting small and medium enterprise with potential export opportunities. Four years later, in May of 2003, they launched Taobao Marketplace, Alibaba’s answer to eBay. By aggressively launching a free to use service, Alibaba’s Taobao quickly became the leading person-to-person trading site in China. In 2018, Taobao GMV (Gross Merchandise Value) was a staggering RMB2,689 billion, which equates to $428 billion in US dollars.
There have been many other successful goods marketplaces that have launched post eBay & Taobao — all providing a similar service of matching those who own or produce goods with a distributed set of buyers who are particularly interested in what they have to offer. In many cases, a deeper focus on a particular category or vertical allows these marketplaces to distinguish themselves from broader marketplaces like eBay.
With the launch of Airbnb in 2008 and Uber(*) in 2009, these two companies established a new category of marketplaces known as the “sharing economy.” Homes and automobiles are the two most expensive items that people own, and in many cases the ability to own the asset is made possible through debt — mortgages on houses and car loans or leases for automobiles. Despite this financial exposure, for many people these assets are materially underutilized. Many extra rooms and second homes are vacant most of the year, and the average car is used less than 5% of the time. Sharing economy marketplaces allow owners to “unlock” earning opportunities from these underutilized assets.
Airbnb was founded by Joe Gebbia and Brian Chesky in 2008. Today there are over 5 million Airbnb listings in 81,000 cities. Over two million people stay in an Airbnb each night. In November of this year, the company announced that it had achieved “substantially” more than $1B in revenue in the third quarter. Assuming a marketplace rake of something like 11%, this would imply gross room revenue of over $9B for the quarter — which would be $36B annualized. As the company is still growing, we can easily guess that in 2019-2020 time frame, Airbnb will be delivering around $50B per year to home-owners who were previously sitting on highly underutilized assets. This is a major “unlocking.”
When Garrett Camp and Travis Kalanick founded Uber in 2009, they hatched the industry now known as ride-sharing. Today over 3 million people around the world use their time and their underutilized automobiles to generate extra income. Without the proper technology to match people who wanted a ride with people who could provide that service, taxi and chauffeur companies were drastically underserving the potential market. As an example, we estimate that ride-sharing revenues in San Francisco are well north of 10X what taxis and black cars were providing prior to the launch of ride-sharing. These numbers will go even higher as people increasingly forgo the notion of car ownership altogether. We estimate that the global GMV for ride sharing was over $100B in 2018 (including Uber, Didi, Grab, Lyft, Yandex, etc) and still growing handsomely. Assuming a 20% rake, this equates to over $80B that went into the hands of ride-sharing drivers in a single year — and this is an industry that did not exist 10 years ago. The matching made possible with today’s GPS and Internet-enabled smart phones is a massive unlocking of wealth and value.
While it is a lesser known category, using your own backyard and home to host dog guests as an alternative to a kennel is a large and growing business. Once again, this is an asset against which the marginal cost to host a dog is near zero. By combining their time with this otherwise unused asset, dog sitters are able to offer a service that is quite compelling for consumers. Rover.com (*) in Seattle, which was founded by Greg Gottesman and Aaron Easterly in 2011, is the leading player in this market. (Benchmark is an investor in Rover through a merger with DogVacay in 2017). You may be surprised to learn that this is already a massive industry. In less than a decade since the company started, Rover has already paid out of half a billion dollars to hosts that participate on the platform.
While not as well known as the goods exchanges or sharing economy marketplaces, there is a growing and exciting increase in the number of marketplaces that help match specifically skilled labor with key opportunities to monetize their skills. The most noteworthy of these is likely Upwork(*), a company that formed from the merger of Elance and Odesk. Upwork is a global freelancing platform where businesses and independent professionals can connect and collaborate remotely. Popular categories include web developers, mobile developers, designers, writers, and accountants. In the 12 months ended June 30, 2018, the Upwork platform enabled $1.56 billion of GSV (gross services revenue) across 2.0 million projects between approximately 375,000 freelancers and 475,000 clients in over 180 countries. These labor matches represent the exact “world is flat” reality outlined in Friedman’s book.
Other noteworthy and emerging labor marketplaces:
These vertical labor marketplaces are to LinkedIn what companies like Zillow, Expedia, and GrubHub are to Google search. Through a deeper understanding of a particular vertical, a much richer perspective on the quality and differentiation of the participants, and the enablement of transactions — you create an evolved service that has much more value to both sides of the transaction. And for those professionals participating in these markets, your reputation on the vertical service matters way more than your profile on LinkedIn.
Having been a fortunate investor in many of the previously mentioned companies (*), Benchmark remains extremely excited about future marketplace opportunities that will unlock wealth on the Internet. Here are an example of two such companies that we have funded in the past few years.
The New York Times describes Hipcamp as “The Sharing Economy Visits the Backcountry.” Hipcamp(*) was founded in 2013 by Alyssa Ravasio as an engine to search across the dozens and dozens of State and National park websites for campsite availability. As Hipcamp gained traction with campers, landowners with land near many of the National and State parks started to reach out to Hipcamp asking if they could list their land on Hipcamp too. Hipcamp now offers access to more than 350k campsites across public and private land, and their most active private land hosts make over $100,000 per year hosting campers. This is a pretty amazing value proposition for both land owners and campers. If you are a rural landowner, here is a way to create “money out of nowhere” with very little capital expenditures. And if you are a camper, what could be better than to camp at a unique, bespoke campsite in your favorite location.
Instawork(*) is an on-demand staffing app for gig workers (professionals) and hospitality businesses (partners). These working professionals seek economic freedom and a better life, and Instawork gives them both — an opportunity to work as much as they like, but on their own terms with regard to when and where. On the business partner side, small business owners/managers/chefs do not have access to reliable sources to help them with talent sourcing and high turnover, and products like LinkedIn are more focused on white-collar workers. Instawork was cofounded by Sumir Meghani in San Franciso and was a member of the 2015 Y-Combinator class. 2018 was a break-out year for Instawork with 10X revenue growth and 12X growth in Professionals on the platform. The average Instawork Professional is highly engaged on the platform, and typically opens the Instawork app ten times a day. This results in 97% of gigs being matched in less than 24 hours — which is powerfully important to both sides of the network. Also noteworthy, the Professionals on Instawork average 150% of minimum wage, significantly higher than many other labor marketplaces. This higher income allows Instawork Professionals like Jose, to begin to accomplish their dreams.
As you can see, these numerous marketplaces are a direct extension of the productivity enhancers first uncovered by Adam Smith and David Ricardo. Free trade, specialization, and comparative advantage are all enhanced when we can increase the matching of supply and demand of goods and services as well as eliminate inefficiency and waste caused by misinformation or distance. As a result, productivity naturally improves.
Specific benefits of global internet marketplaces:
Source : http://abovethecrowd.com/2019/02/27/money-out-of-nowhere-how-internet-marketplaces-unlock-economic-wealth/
At a recent KPMG Robotic Innovations event, Futurist and friend Gerd Leonhard delivered a keynote titled “The Digital Transformation of Business and Society: Challenges and Opportunities by 2020”. I highly recommend viewing the Video of his presentation. As Gerd describes, he is a Futurist focused on foresight and observations — not predicting the future. We are at a point in history where every company needs a Gerd Leonhard. For many of the reasons presented in the video, future thinking is rapidly growing in importance. As Gerd so rightly points out, we are still vastly under-estimating the sheer velocity of change.
With regard to future thinking, Gerd used my future scenario slide to describe both the exponential and combinatorial nature of future scenarios — not only do we need to think exponentially, but we also need to think in a combinatorial manner. Gerd mentioned Tesla as a company that really knows how to do this.
He then described our current pivot point of exponential change: a point in history where humanity will change more in the next twenty years than in the previous 300. With that as a backdrop, he encouraged the audience to look five years into the future and spend 3 to 5% of their time focused on foresight. He quoted Peter Drucker (“In times of change the greatest danger is to act with yesterday’s logic”) and stated that leaders must shift from a focus on what is, to a focus on what could be. Gerd added that “wait and see” means “wait and die” (love that by the way). He urged leaders to focus on 2020 and build a plan to participate in that future, emphasizing the question is no longer what-if, but what-when. We are entering an era where the impossible is doable, and the headline for that era is: exponential, convergent, combinatorial, and inter-dependent — words that should be a key part of the leadership lexicon going forward. Here are some snapshots from his presentation:
Source: B. Joseph Pine II and James Gilmore: The Experience Economy
Gerd then summarized the session as follows:
The future is exponential, combinatorial, and interdependent: the sooner we can adjust our thinking (lateral) the better we will be at designing our future.
My take: Gerd hits on a key point. Leaders must think differently. There is very little in a leader’s collective experience that can guide them through the type of change ahead — it requires us all to think differently
When looking at AI, consider trying IA first (intelligent assistance / augmentation).
My take: These considerations allow us to create the future in a way that avoids unintended consequences. Technology as a supplement, not a replacement
Efficiency and cost reduction based on automation, AI/IA and Robotization are good stories but not the final destination: we need to go beyond the 7-ations and inevitable abundance to create new value that cannot be easily automated.
My take: Future thinking is critical for us to be effective here. We have to have a sense as to where all of this is heading, if we are to effectively create new sources of value
We won’t just need better algorithms — we also need stronger humarithms i.e. values, ethics, standards, principles and social contracts.
My take: Gerd is an evangelist for creating our future in a way that avoids hellish outcomes — and kudos to him for being that voice
“The best way to predict the future is to create it” (Alan Kay).
My Take: our context when we think about the future puts it years away, and that is just not the case anymore. What we think will take ten years is likely to happen in two. We can’t create the future if we don’t focus on it through an exponential lens
Source : https://medium.com/@frankdiana/digital-transformation-of-business-and-society-5d9286e39dbf
Digital health innovation continues moving full-force in transforming the business of healthcare. For pharma and medtech companies in particular, this ongoing shift has pushed them to identify ways to create value for patients beyond the drugs themselves. From new partnerships between digital health and life science companies to revamped commercial models, collecting and extracting insights from data is at the core of these growth opportunities. But navigating the rapidly evolving terrain is no simple task.
To help these companies effectively incorporate and utilize digital health tools, Rock Health partner ZS Associates draws on over 30 years of industry expertise to guide them through the complex digital health landscape. We chatted with Principal Pete Masloski to discuss how he works with clients to help identify, develop, and commercialize digital health solutions within their core businesses—and where he sees patients benefiting the most as a result.
Note: This interview has been lightly edited for clarity.
Where does ZS see the promise of data- and analytics-enabled digital health tools leading to in the next five years, 10 years, and beyond?
Data and analytics will play a central role in the digital health industry’s growth over the next five to ten years. Startups are able to capture larger, novel sets of data in a way that large life science companies historically have not been able to. As a result, consumers will be better informed about their health choices; physicians will have more visibility into what treatment options work best for whom under what circumstances; health plans will have a better understanding of treatment choices; and pharmaceutical and medical device companies will be able to strategically determine which products and services to build.
We see personalized medicine, driven by genomics and targeted therapies, rapidly expanding over the next few years. Pharmaceutical discovery and development will also transition to become more digitally enabled. The ability to match patients with clinical trials and improve the patient experience will result in lower costs, faster completion, and more targeted therapies. The increase in real-world evidence will be used to demonstrate the efficacy of therapeutics and devices in different populations, which assures payers and providers that outcomes from studies can be replicated in the real world.
How is digital health helping life sciences companies innovate their commercial models? What is the role of data and analytics in these new models?
The pharmaceutical industry continues to face a number of challenges, including the increasingly competitive markets, growing biosimilar competition, and overall scrutiny on pricing. We’ve seen excitement around solutions that integrate drugs with meaningful outcomes and solutions that address gaps in care delivery and promote medication adherence.
Solving these problems creates new business model opportunities for the industry through fresh revenue sources and ways of structuring agreements with customers. For example, risk-based contracts with health plans, employers, or integrated delivery networks (IDNs) become more feasible when you can demonstrate increased likelihood of better outcomes for more patients. We see this coming to fruition when pharma companies integrate comprehensive digital adherence solutions focused on patient behavior change around a specific drug, as in Healthprize’s partnership with Boehringer Ingelheim. In medtech, digital health tools can both differentiate core products and create new profitable software or services businesses. Integrating data collection technology and connectivity into devices and adding software-enabled services can support a move from traditional equipment sales to pay-per-use. This allows customers to access the new equipment technology without paying a large sum up front—and ensures manufacturers will have a more predictable ongoing source of revenue.
That said, data and analytics remain at the core of these new models. In some cases, such as remote monitoring, the data itself is the heart of the solution; in others, the data collected helps establish effectiveness and value as a baseline for measuring impact. Digital ambulatory blood pressure monitors capture an individual’s complete blood pressure profile throughout the day, which provides a previously unavailable and reliable “baseline.” Because in-office only readings may be skewed by “white coat hypertension,” or stress-induced blood pressure readings, having a more comprehensive look at this data can lead to deeper understandings of user behaviors or conditions. Continuous blood pressure readings can help with diagnoses of stress-related drivers of blood pressure spikes, for example. These insights become the catalyst for life science companies’ new product offerings and go-to-market strategies.
What are some examples of how data sets gathered from partnerships with digital health companies can be leveraged to uncover new value for patients and address their unmet needs?
As digital health companies achieve a certain degree of scale, their expansive data sets become more valuable because of the insights that can be harnessed to improve outcomes and business decisions. Companies like 23andMe, for example, have focused on leveraging their data for research into targeted therapies. Flatiron Health is another great example of a startup that created a foundational platform (EMR) whose clinical data from diverse sources (e.g., laboratories, research repositories, and payer networks) became so highly valued in cancer therapy development that Roche acquired it earlier this year for close to $2B.
It’s exciting to think about the wide array of digital health solutions and the actionable insight that can be gleaned from them. One reason partnerships are important for the industry is few innovators who are collecting data have the capabilities and resources to fully capitalize on its use on their own. Pharma companies and startups must work together to achieve all of this at scale. Earlier this year, Fitbit announced a new partnership with Google to make the data collected from its devices available to doctors. Google’s API can directly link heart rate and fitness activity to the EMR, allowing doctors to easily review and analyze larger amounts of data. This increase in visibility provides physicians with more insight into how patients are doing in between visits, and therefore can also help with decision pathways.
Another example announced earlier this year is a partnership between Evidation Health and Tidepool, who are conducting a new research study, called the T1D Sleep Pilot, to study real-world data from Type 1 diabetics. With Evidation’s data platform and Tidepool’s device-agnostic consumer software, the goal is to better understand the dynamics of sleep and diabetes by studying data from glucose monitors, insulin pumps, and sleep and activity trackers. The data collected from sleep and activity trackers in particular allows us to better understand possible correlations between specific chronic conditions, like diabetes, and the impact of sleep—which in the past has been challenging to monitor. These additional insights provide a more comprehensive understanding of a patient’s condition and can lead to changes in treatment decisions—and ultimately, better outcomes.
How do you assess the quality and reliability of the data generated by digital health companies? What standards are you measuring them against?
Data quality management (DQM) is the way in which leading companies evaluate the quality and reliability of data sources. ISO 9000’s definition of quality is “the degree to which a set of inherent characteristics fulfills requirements.” At ZS, we have a very robust DQM methodology, and our definition goes beyond the basics to include both the accuracy and the value of the data. Factors such as accuracy and absence of errors, and fulfilling specifications (business rules, designs, etc.), are foundational, but in our experience it’s most important to also include an assessment of value, completeness, and lack of bias because often these factors can lead to misleading or inaccurate insights from analysis of that data.
However, it’s not easy assessing the value of a new data source, which presents an entirely different set of challenges. One very important one is the actual interpretation of the data that’s being collected. How do you know when someone is shaking their phone or Fitbit to inflate their steps, or how do you interpret that the device has been taken off and it’s not tracking activity? How do you account for that and go beyond the data to understand what is really happening? As we get more experience with IOT devices and algorithms get smarter, we will get better at interpreting what these devices are collecting and be more forgiving of underlying data quality.
What are the ethical implications or issues (such as data ownership, privacy, and bias) you’ve encountered thus far, or anticipate encountering in the near future?
The ethical stewardship and protection of personal health data are just as essential for the long-term sustainability of the digital health industry as the data itself. The key question is, how can the industry realize the full value from this data without crossing the line? Protecting personal data in an increasingly digitized world—where we’ve largely become apathetic to the ubiquitous “terms and conditions” agreements—is a non-negotiable. How digital health and life science companies collect, manage, and protect users’ information will remain a big concern.
There are also ethical issues around what the data that is captured is used for. Companies need to carefully establish how to appropriately leverage the data without crossing the line. For example, using de-identified data for research purposes with the goal of improving products or services is aligned with creating a better experience for the patient, as opposed to leveraging the data for targeted marketing purposes.
Companies also face the issue of potential biases that may emerge when introducing AI and machine learning into decision-making processes around treatment or access to care. Statistical models are only as good as the data that are used to train them. Companies introducing these models need to test datasets and their AI model outputs to ensure gaps are eliminated from training data, the algorithms don’t learn to introduce bias, and they establish a process for evaluating bias as the models continue to learn and evolve.
Source : https://rockhealth.com/the-data-driven-transformation-of-the-life-sciences-industry-a-qa-with-zs-associates-pete-masloski/
As designers, we want to work on problems that are intriguing and “game-changing”. All too often, we limit the “game-changing” category to a handful of consumer-facing mobile apps and social networks. The truth is: enterprise software gives designers a unique set of complex problems to solve. Enterprise platforms usually have a savvy set of users with very specific needs — needs that, when addressed, often affect a business’s bottom line.
One of my first projects as a product designer here at Instacart was to redesign elements of our inventory management tool for retailers (e.g. Kroger, Publix, Safeway, Costco, etc.). As I worked on the project more and more, I learned that Enterprise tools are full of gnarly complexity and often present opportunities to practice deep thought. As Jonathan, one of our current enterprise platform designers said —
The greater the complexity, the greater the opportunity to find elegance.
As we scoped the project we found that the existing product wasn’t enabling retailers to manage their inventories as concisely and efficiently as they could. We found retailer users were relying on customer support to help carry out smaller tasks. Our goal with the redesign was to build and deliver a better experience that would enable retailers to manage their inventory more easily and grow their business with Instacart.
The first step in redesigning was to understand the flow of the current product. We mapped out the journey of a partner going through the tool and spoke with the PMs to figure out what we could incorporate into the roadmap.
Once we had a good understanding of the lay of the land, engineering resources, and retailers’ needs, we got into the weeds. Here are a few improvements we made to the tool —
We used the department tiles feature from our customer-facing product as the catalog’s landing page (1.0 above). With this, we worked to:
Our solution simplified a few things:
While it’s great that the older Item Details page was partitioned into sections, from an IA perspective, it offered challenges for two reasons:
To address this, we broke down the sections into what’s truly necessary. From there, we identified four main categories of information that the data fell under:
Sources now pop up on the top right of the input fields so the editor knows who last made changes.
Seeking validation through numbers is always fantastic. We did a small beta launch of this product and saw an increase in weekly engagement and decrease in support requests.
I learned that designing enterprise products helps you extend yourself as a visual designer and deep product thinker. I approached this project as an opportunity to break down complex interactions and bring visual elegance to a product through thoughtful design. To this day, it remains one of my favorite projects at Instacart as it stretched my thinking and enhanced my visual design chops. Most importantly, it taught me to look at Enterprise tools in a new light; now when I look at them, I am able to appreciate the complexity within
What a great #AWE2018 show in Munich, with a strong focus on the industry usage and, of course , the german automotive industry was well represented. Some new , simple but efficient, AR devices , and plenty of good use cases with a confirmed ROI. This edition was PRAGMATIC.
The use of XR by automotive companies, big pharma, and teachers confirmed some good ROI with some “ready to use” solutions, especially in this domains :
To create specific and advanced AR Apps, there is still some challenges with the content authoring and with the integration to the legacy systems to retrieve master data and 3D assets. Automotized and integrated AR app need some ingenious developments.
An interesting use case from Boeing ( using hololens to assist the mounting of cables) shows how they did to get an integrated and automatized AR app. Their AR solution architecture in 4 blocks :
The usage of AR and VR becomes more important in many domains : From conception to maintenance and sales (configurator, catalogs …)
The consequence is that original CAD files can be transformed and used in different processes of your company, where it becomes a challenge to use high polygon from CAD applications into other 3D / VR / AR applications, where there is a need of lighter 3D assets, also with some needs of texture and rendering adjustment.
gIFT can be a solution , glTF defines an extensible, common publishing format for 3D content tools and services that streamlines authoring workflows and enables interoperable use of content across the industry.
The main challenge is to implement a good centralised and integrated 3D asset management strategy, considering them as important as your other key master data.
The conception of advanced and integrated AR solutions for large companies needs some new expert combining knowlegde in 3D apps and experience in system integration.
This projects need new types of information system architecture taking in account the AR technologies.
PTC looks like a leader in providing efficient and scalable tools for large companies. PTC, owner of Vuforia is also exceling with other 3D / PLM management solutions like windchill , to smoothly integrate 3D management in all the processes and IT of the enterprise.
Sopra Steria , the french IS integration company, is also taking this role , bringing his system integration experience into the new AR /VR usages in the industry.
If you don’t want to invest in this kind of complex projects, for a first step in AR/VR or for some quick wins at a low budget , new content authoring solutions exist to build your AR app with some simple user interfaces and workflows : skylight by Upskill , worklink by Scope AR
“A real time 3D (or spatial) map of the world, the AR cloud, will be the single most important software infrastructure in computing. Far more valuable than facebook social graph, or google page rank index” say Ori Inbar, Co-Founder and CEO of Augmented Reality.ORG. A promising prediction.
The AR cloud provide a persistant , multiuser and cross device AR landscape. It allows people to share experiences and collaborate. The most known AR cloud experience so far is the famous Pokemon Go game.
So far the AR map works using GPS or image recognition, or local point of cloud for a limited space / a building. The dream will be to copy the world as a point of cloud, for a global AR cloud landscape. A real time systems that could be used by robots, drones etc…
The AWE exhibition presented some interesting AR cloud initiative :
Source : https://www.linkedin.com/pulse/augmented-reality-state-art-industry-fr%C3%A9d%C3%A9ric-niederberger/
As more consumers search for sustainable packaging options, food and beverage companies are forced to make tough decisions about their products.
Shoppers and investors are increasingly looking for companies and brands to take the initiative on environmental issues. A Horizon Media study found 81% of millennials expect companies to make public commitments to good corporate citizenship and 66% of consumers will pay more for products from brands committed to environmentally friendly practices, according to the Nielsen Global Corporate Sustainability Report.
But more eco-friendly practices haven’t been easy for the food packaging industry. Designing eco-friendly packaging that can keep products fresh and endure temperature changes that come with cooking can be a challenge. Packaging companies told Food Dive they recently made moves to offer sustainable options with water-based ink and more compostable packaging, but have faced obstacles along the way. While some brands are aiming to only appear more sustainable, others are making slow efforts to be eco-friendly with new innovations and products.
For major food and beverage companies, the higher cost of sustainable materials and the struggle to keep food fresh are barriers. Production costs for sustainable options be about 25% more compared to traditional packaging. These materials also tend to be less effective in maintaining freshness, since packaging companies say plastic can have a tighter seal and keep out air better than other materials.
“That’s their compromise, it looks eco-friendly — but it’s not.” Damon Leach – Account representative at Green Rush Packaging
Some companies have found a way around the high costs. Damon Leach, an account representative at Green Rush Packaging, told Food Dive that a solution for some food companies has been to use material that looks recyclable to shoppers, but in reality, is not.
Instead of paying more for eco-friendly materials, companies have been picking material, like kraft paper, that looks more sustainable to consumers, he added. Leach said the products that appear to be more green do sell better.
Although Leach said more suppliers and consumers theoretically want sustainable packaging, those materials typically don’t have a long shelf life and consumers don’t want to pay the extra money. But some companies are still making an effort to pay more for eco-friendly packaging despite the challenges.
From producers and companies to retailers, consumers and recycling organizations, packaging can affect the whole supply chain. So the challenge for packaging manufacturers becomes determining what new innovations and materials are the best investments.
Randall LaVeau, business development manager at Interpress Technologies, which manufactures formed paperboard and plastic food packaging products, told Food Dive there is a huge push for more recycling in the marketplace. But he said it is hard to get an eco-friendly material that holds water but isn’t plastic and doesn’t degrade — a necessity for microwavable products that need water to cook.
Many companies are now working to develop recyclable packaging that can withstand heat and hold liquids, but LaVeau said there is still a lot of research and development to go before it is widespread.
“Everybody is in the shop trying to figure it out,” LaVeau said. “People have been working on it for the last 10 years or longer, they just haven’t had a good success for it.”
For companies that have made sustainability goals, the time is ticking to figure it out. Mondelez just announced its plans to make all their packaging recyclable by 2025. Nestlé, Unilever and PepsiCo have agreed to phase in packaging made from recyclable, compostable and biodegradable materials with more recycled content by 2025, but haven’t released specific details about their plans. In fact, a recent report identified Coca-Cola, PepsiCo and Nestle as businesses contributing most to pollution.
But as these big companies push for more development on sustainable materials, that means cost could continue to be an obstacle. Although consumers say they are willing to pay more for sustainability, they don’t always pick up the more expensive options in stores.
“Just like anything else, when something new comes out… it is more expensive until they can work with it in time and maximize their efficiencies for the cost to come down,” LaVeau said.
Several companies have developed more sustainable options this year. For example, HelloFresh is rolling out more sustainable packaging for its meal kits with recyclable liners created by sustainable design company TemperPack.
And some new developments haven’t come into the mainstream yet. U.S. Department of Agriculture researchers have developed an edible, biodegradable packaging film made of casein, a milk protein, that can be wrapped around food to prevent spoilage.
Other companies are working to find new ways to help the environment. Wayne Shilka, vice president of innovation and technical support at Eagle Flexible Packaging, a printer of packaging in Chicago, has prioritized offering more sustainable options to their customers. Eagle Flexible Packaging uses a water-based ink because it doesn’t create any probable organic compounds that then go out into the atmosphere, making it more environmentally friendly, Shilka said.
“We are finding that sustainable packaging is getting more and more and more interest.”, Wayne Shilka – Vice president of Eagle Flexible Packaging
Six years ago, Eagle Flexible Packaging put together a compostable material for packaging, and about 100 companies discussed the option with them. Only one customer ended up using the compostable product because it cost more any other packaging option the company offered. Every year since, a few more customers have worked with them to outfit their products with compostable material, Shilka said.
As more companies turn to compostable and sustainable packaging, the price will come down and make it more appealing, Shilka added.
“It continues growing to the point that it’s becoming not mainstream, but it’s much more routine that we had people who are calling and are interested and are actually doing something sustainable,” Shilka said.
While some companies work to find new recyclable materials, others are satisfied with current packaging options. Flexible packaging — which is any package whose shape can be readily changed, such as bags and pouches — is popular. Representatives at packaging companies said flexible packaging can be an issue for sustainability since it has multilayer films with plastic and paper that need to be separated to be recycled.
LaVeau said most of his products are “recyclable to an extent” because of the layers. Certain recycling mills can handle his products, but at others, consumers need to separate the packaging for recycling — which doesn’t always happen.
Green Rush Packaging has the same issue.
“We got to get the end users to separate and recycle better instead of just facilities otherwise it is just waste, bad for the environment,” Leach said.
Flexible packaging can also provide a higher product-to-package ratio, which creates fewer emissions during transportation and ultimately uses less space in landfills.
Some companies stand by their use of packages that aren’t fully sustainable. Robert Reinders, president of Performance Packaging, a family owned corrugated box plant founded in 1995 by packaging professionals, told Food Dive that about 5% of his products are recyclable. He said flexible packaging is a sustainable option because it uses up less energy and prolongs the shelf life of the food so it eliminates food waste.
“There is all kinds of great benefits to flexible packaging that gets drowned out by the recycle, compostable needs,” Reinders said.
In the last two years, more than 70 bills have been introduced in state legislatures regarding plastic bags — encompassing bans, fees and recycling programs. However, many of those laws have not impacted the food packaging industry.
In comparison, countries across the globe are increasing their efforts and goals when it comes to sustainability for both food and beverage product packaging. But U.S. companies are still in the development stage on many of their innovations.
The Singapore Packaging Agreement — a joint initiative by government, industry and NGOs to reduce packaging waste — has averted about 46,000 metric tons of packaging waste during the past 11 years, according to Eco-Business. In Australia, national, state and territory environment ministers have agreed that 100% of Australian packaging will be recyclable, compostable or reusable by 2025.
Vancouver, Canada has adopted a ban on the distribution of polystyrene foam cups and containers, as well as restrictions on disposable cups and plastic shopping bags. The U.K. also plans to eliminate plastic waste by 2042.
As countries around the world change their packaging to adjust to these sustainability goals, Reinders said U.S. companies will likely adopt more changes. And as more CPG makers start mass producing sustainable options around the world, he said it will drive prices down globally.
“I was at Nestlé headquarters in Switzerland and they are currently making the efforts to find different materials and different processes so they can be recyclable,” Reinders said. “It’s all starting now. The more the big guys get into it, the better it will be.”
Source : https://www.fooddive.com/news/how-sustainable-is-the-food-packaging-industry/539089/
San Francisco is known for its famous neighborhoods and commercial corridors — and the Mission District’s Valencia Street takes it to the next level. For Lyft, Valencia Street is filled with top destinations that our passengers frequent: trendy cafes, hipster clothing stores, bars, and live music.
To put it simply, there’s a lot happening along Valencia Street. Besides the foot traffic, many of its restaurants are popular choices on the city’s growing network of courier services, providing on-demand food delivery via cars and bicycles. Residents of the Mission are increasingly relying on FedEx, Amazon, and UPS for stuff. Merchants welcome commercial trucks to deliver their goods. In light of a recent road diet on Mission Street to create much needed dedicated lanes to improve MUNI bus service, many vehicles have been re-routed to parallel streets like Valencia. And of course, Valencia Street is also one of the most heavily trafficked bicycling corridors in the City, with 2,100 cyclists commuting along Valencia Street each day.
With so many different users of the street and a street design that has largely remained unchanged, it’s no surprise that the corridor has experienced growing safety concerns — particularly around increased traffic, double parking, and bicycle dooring.
Valencia Street is part of the City’s Vision Zero High-Injury Network, the 13% of city streets that account for 75% of severe and fatal collisions. From January 2012 to December 2016, there were 204 people injured and 268 reported collisions along the corridor, of which one was fatal.
As the street has become more popular and the need to act has become more apparent, community organizers have played an important role in rallying City forces to commit to a redesign. The San Francisco Bicycle Coalition has been a steadfast advocate for the cycling community’s needs: going back to the 1990s when they helped bring painted bike lanes to the corridor, to today’s efforts to upgrade to a protected bike lane. The People Protected Bike Lane Protests have helped catalyze the urgency of finding a solution. And elected officials, including Supervisor Ronen and former Supervisor Sheehyhave been vocal about the need for change.
Earlier this spring, encouraged by the SFMTA’s first steps in bringing new, much-needed infrastructure to the corridor, we began conducting an experiment to leverage our technology as part of the solution. As we continue to partner closely with the SFMTA as they work on a new design for the street, we want to report back what we’ve learned.
As we began our pilot, we set out with the following goals:
To meet these goals, we first examined Lyft ride activity in the 30-block project area: Valencia Street between Market Street and Cesar Chavez.
Within this project area, we found that the most heavily traveled corridors were Valencia between 16th and 17th Street, 17th and 18th Street, and 18th and 19th Street. We found that these three blocks make up 27% of total Lyft rides along the Valencia corridor.
We also wanted to understand the top destinations along the corridor. To do this, we looked at ride history where passengers typed in the location they wanted to get picked up from.
Next, we looked at how demand for Lyft changed over time of day and over the course of the week. This would help answer questions such as “how does demand for Lyft differ on weekends vs. weeknights” or “what times of day do people use Lyft to access the Valencia corridor?”
We found that Lyft activity on Valencia Street was highest on weekends and in the evenings. Demand is fairly consistent on weekdays, with major spikes of activity on Fridays, Saturdays, and Sundays. The nighttime hours of 8 PM to 2 AM are also the busiest time for trips, making up 44% of all rides. These findings suggest the important role Lyft plays as a reliable option when transit service doesn’t run as frequently, or as a safe alternative to driving under the influence (a phenomenon we are observing around the country).
Our hypothesis was that because of the increased need for curb space between multiple on-demand services, as well as the the unsafe experience of double parking or crossing over the bike lane to reach passengers, improvements in the Lyft app could help create a better experience for everyone.
To test this, our curb access pilot program was conducted as an “A/B experiment”, where subjects were randomly assigned to a control or treatment group, and statistical analysis is used to determine which variation performs better. 50% of riders continued to have the same experience requesting rides within the pilot area: able to get picked up wherever they wanted. The other 50% of Lyft passengers requesting rides within the pilot zone were shown the experiment scenario, which asked them to walk to a dedicated pickup spot.
Geofencing and Venues
Our pilot was built using a Lyft feature called “Venues”, a geospatial tool designed to recommend pre-set pickup locations to passengers. When a user tries to request a ride from an area that has been mapped with a Venue, they are unable to manually control the area in which they’d like to be picked up. Rather, the Venue feature automatically redirects them to a pre-established location. This forced geofencing feature helps ensure that passengers are requesting rides from safe locations and build reliability and predictability for both passengers and drivers as they find each other.
Given our understanding of ride activity and demand, we decided to create Venues on Valencia Street between 16th Street and 19th Street. We prioritized creating pickup zones along side streets in areas of lower traffic. Where possible, we tried to route pickups to existing loading zones: however, a major finding of the pilot was that existing curb space is insufficient and that the city needs more loading zones. To support better routing and reduce midblock u-turns or other unsafe driving behavior, we tried to put pickup spots on side streets that allowed for both westbound and eastbound directionality.
Our pilot ran for three months, from March 2018 to June 2018. Although our initial research focused on rideshare activity during hours of peak demand (i.e. nights and weekends), to support our project goals of increasing overall safety along the corridor and to create an easy and intuitive experience for passengers, we ultimately decided to run the experiments 24/7.
The graphic below illustrates where passengers were standing when they requested a ride, and which hotspot they were redirected to. We found that the top hot spots were on 16th Street. This finding suggests the need for continued coordination with the City to make sure that the dedicated pickup spots to protect cyclists on Valencia Street don’t interrupt on-time performance for the 55–16th Street or 22–Fillmore Muni bus routes.
Loading time, when a driver has pulled over to wait for a passenger to arrive or exit their car, was important for us to look at in terms of traffic flow. This is a similar metric to the transportation planning metric, dwell time.
Currently, our metric for loading time looks at the time between when a driver arrives at the pickup location and when they press the “I have picked up my passenger” button. However, this is an imperfect measurement for dwell time, as drivers may press the button before the passenger gets in the vehicle. Based on our pilot, we have identified this as an area for further research.
Going into our experiment, we expected to see a slight increase in loading time, as passengers would need to get used to walking to the pickup spot. This hypothesis was correct: during the pilot, we saw loading time increased from an average of 25 seconds per ride to 28 seconds. To help speed up the process of drivers and passengers finding each other, we recommend the addition of wayfinding and signage in popular loading areas.
We also wanted to understand the difference between pickups and drop-offs. Generally, we found that pickups have a longer loading time than a drop-off.
Ridesharing is one part of the puzzle to creating a more organized streetscape along the Valencia corridor, so sharing information and coordinating with city stakeholders was critical. After our experiment, we sat down with elected officials, project staff from the SFMTA, WalkSF, and the San Francisco Bicycle Coalition to discuss the pilot findings and collaborate on how our work could support other initiatives underway across the city. We are now formally engaged with the SFMTA’s Valencia Bikeway Improvement Project and look forward to continuing to support this initiative.
Given the findings of this pilot program and our commitment to creating sustainable streets (including our acquisition of the leading bikeshare company Motivate and introduction of bike and scooter sharing to the Lyft platform), we decided to move our project from a pilot to a permanent featurewithin the Lyft app. This means that currently, anyone requesting a ride on Valencia Street between 16th Street and 19th Street will be redirected to a pickup spot on a side street.
Based on the learnings of our pilot, we recommend the following:
We know that ridesharing is just one of the many competing uses of Valencia Street and technology alone will not solve the challenges of pickups and drop-offs: adequate infrastructure like protected bike lanes and loading zones will be necessary to achieving Vision Zero.
Looking ahead, we know there’s much to be done on this front. To start with, we are are excited to partner with civic engagement leaders like Streetmixwhose participatory tools ensure that public spaces and urban design support safe streets. By bringing infrastructure designs like parking protected bike lanes or ridesharing loading zones into Streetmix, planners can begin to have the tools to engage community groups on what they’d like to see their streets look like.
We’ve also begun partnering with Together for Safer Roads to support local bike and pedestrian advocacy groups and share Lyft performance data to help improve safety on some of the nation’s most dangerous street corridors. And finally, through our application to the SFMTA to become a permitted scooter operator in the City, we are committing $1 per day per scooter to support expansion of the City’s protected bike lane network. We know that this kind of infrastructure is critical to making safer streets for everyone.
We know that this exciting work ahead cannot be done alone: we look forward to bringing this type of work to other cities around the country and to working together to achieve this vision
Source : https://medium.com/@debsarctica/creating-a-safer-valencia-street-54c25a75b753
It’s your first day at a new job, and you’re stuck going to corporate training. But instead of going to a training room and listening to the HR director drone on about the vacation policy, you’re directed to the IT desk.
You’re greeted there by a friendly college intern. “Welcome to work,” she says, handing you a box. “Here’s your VR headset. Enjoy orientation!”
Gut check: How do you feel about this scenario?
A recent survey found that approximately half of professionals would be interested in learning something new in a virtual reality environment.
So regardless of whether the idea of virtual training makes your heart skip a beat or stop altogether, you’re in pretty good company.
The question is — is this the right direction?
It might be. There hasn’t been a ton of research done on the educational uses of VR, but the one in-depth study that has been done was optimistic.
Researchers created a virtual castle (“the memory palace,” named for an old mnemonic device of the same name) and placed pictures of various celebrities throughout it.
One group of research subjects got to explore the castle using a VR headset while another group explored it using a computer and mouse.
After exploring for a while, each group took a break for two minutes and then went back to the castle. This time, all of the pictures were replaced with question marks, and the subjects had to remember which picture went where.
And—shocker—the VR users did better than the computer users. Therefore, obviously, VR is a more effective teaching tool than a computer—right?
Not so fast. Even though VR beat out desktop computers in the memory palace, there are other factors to consider when it comes to education.
One possible explanation for VR’s success in this study is the novelty effect, which basically means that newer technology will always give an initial bump in learning outcomes just because it’s new.
Chances are, at least some of the subjects who got to use VR to explore the memory palace were having their very first VR experience. Their minds were blown. Their interest in the new technology probably drove them to explore every nook and cranny of their virtual environment and memorize as many details as possible.
Meanwhile, their counterparts on the desktop computers sat there, completely jaded to the mystical connection of mouse and screen.
But the novelty effect alone shouldn’t make us completely discount these findings. A couple decades ago, researchers were doing similar tests comparing computers to TVs and frankly being a little overly pessimistic that computers would be the wave of the future.
Now that we live in a world where TVs and computers are equally un-novel, I don’t think you’ll find anyone who would argue that Schoolhouse Rock is a more effective teaching tool than Khan Academy, even if the songs are better.
Let’s imagine a slight variation of the memory palace experiment. Test subjects still explore a castle, and they still find portraits of celebrities, but this time the portraits include some biographical information about the celebrity.
Then, after they’ve explored the castle and taken a two minute break, the subjects have to recall as much biographical information about as many celebrities as possible.
My hypothesis for this experiment would be that the computer users would actually perform better. Reading information off of a computer screen and recalling it later is old hat.
The test subjects in the VR environment would be so excited about exploring their surroundings that they wouldn’t want to take the time to read and retain information.
But in the actual experiment, where the subjects needed to remember the location of pictures instead of biographic data, the propensity to run around and explore was beneficial in completing the task at hand.
The more enjoyment you get out of exploring your surroundings, the more likely you are to remember details about what you see. In that situation, it should come as no surprise that VR brought better results than a desktop computer.
Another possible follow-up study would involve physically constructing the memory palace and seeing how well people recall memories of a real castle compared to a VR one.
My money would be on VR as long as the technology retains its novelty, but if it ever becomes as mundane as a desktop computer, I bet the real castle would produce better results.
Why? Because the actual task is to recall something’s position. Where in the castle did you see the picture of Marilyn Monroe? Where did you see the picture of Mickey Mouse? The more directly you can interact with the space you’re trying to locate an object in, the better your recall is bound to be.
The real challenge in applying VR to education is figuring out which topics VR would be the best teaching platform for.
This is not a new concept. There’s been an ongoing debate about online vs. in-person training for as long as online training has been an option.
And there’s really only one answer that’s always right: It depends on what you’re teaching. So VR won’t ever be the best tool for every single learning situation because no educational tool can ever claim that honor.
But VR education will be very well suited to situations where physical location matters—but only when a non-virtual version of that location is unavailable.
For example, if you really are welcoming employees to their first day of work, it would probably be more effective to give them a real tour of the actual building instead of having them wander through a virtual reproduction.
But if you’re teaching interior design or training a batch of private investigators, VR might be the best possible educational tool.
And let’s not forget about AR (augmented reality). Instead of providing a fully immersive experience like VR does, AR inserts virtual elements into your view of your real surroundings. AR is currently being explored as a platform for training firefighters.
This is a perfect match. You can have your firefighters or other rescue workers running around in a real building while dealing with virtual hazards. This might be the best possible way to teach a dangerous job in a safe environment.
But not all of us need to take (or deliver) that kind of training. If you’re an HR director tasked with explaining medical benefits to a batch of new hires, maybe it’s best to stick with powerpoint for now.