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From the community | An open letter to the Stanford administration concerning alcohol culture on campus – The Stanford Daily

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Our names are Lea, Clarisse and Liza, and we are the co-directors of the 5-SURE on Foot program on campus, a student-led health and safety initiative that provides snacks and water and walks students home on the weekends. We are writing today out of concern for attitudes and behaviors toward alcohol on Stanfords campus. As three people who work in alcohol safety, we witness parties on campus every single week. This quarter, we have watched unhealthy behaviors toward alcohol increase rapidly and frighteningly. We believe there are clear reasons for this increase, and we ask that the Stanford administration take the appropriate steps to address this alarming trend.

At the 5-SURE on Foot table, we know that significantly more parties than are registered occur every weekend. We have had nights with twice the usual numbers of people visiting the table; on one night, over 1300 students came to the table. While this in itself is not a bad thing, it is indicative of a large increase in the number of students engaging in parties and alcohol consumption. We have had to intervene in many more alcohol poisoning incidents than usual. Previously, we rarely had to deal with vomiting in our role, but this year, we have had several instances of vomiting at the table, and some students have been so intoxicated that they were extremely disoriented and could not stand even with assistance. We have also had to deal with near-transports or transports every weekend.Excessive alcohol consumption additionally poses hazards, including running in the dark across busy streets, disregard for COVID-19 safety, and property damage.

There are several plausible explanations for this recent change. The first is perceived attitudes toward drinking and partying culture on campus. New students have just been thrown into a completely new physical, academic and social environment. Academic stress and social interactions encourage students to push their boundaries without knowing their tolerance or comfort limits. This is, of course, not a novel occurrence; however, we have heard from students personally that they feel they need to hide drinking or do not feel comfortable asking for help if something happens while drinking. In addition, during the times we have intervened with alcohol poisoning incidents at the table, students become very nervous when we suggest calling a transport, the RD on call or even their RAs.

We have heard from students that another reason for the increase in dangerous drinking behaviors is the lack of alternative substance-free programming on campus. With the disappearance of Cardinal Nights, many students who dont drink, or students who still want to go out with friends but dont feel comfortable drinking, have no other structured nighttime entertainment options. We have heard from RAs that dormitories lack sufficient funding to provide consistent alternative programming, too. As a result, students feel there is nothing else to do on weekends except drink and go to parties, which both excludes students who dont drink and pressures students who dont know their limits to engage in drinking past their boundaries.

We believe there are clear ways to address this concerning increase in unhealthy alcohol-related behaviors. The first is to reinstate and increase substance-free programming, including Cardinal Nights. Prior to the start of the COVID-19 pandemic, Cardinal Nights provided university-sponsored substance-free events 2-3 nights per week. These events ranged from on-campus socials and craft nights to tickets for off-campus movies and concerts. The presence of Cardinal Nights improved campus morale and was an exciting and reassuring source of entertainment for all students. In addition, the presence of student staff at Cardinal Nights events was critical for student engagement in their events because students felt both more excited and comfortable attending events run by their peers. We also need to provide the resources and support for student dorms to host alternative programming and think carefully and holistically about attitudes toward handling substance use culture on campus. In part, the reason why Cardinal Nights programming was so beneficial was because it created a social environment that removed alcohol as the primary motivator, encouraging students to be clear and honest about their own boundaries and comfort levels. This way, we can create a campus culture that prioritizes students safety and positive peer interactions.

Please take the necessary steps to create a safe and healthy social environment for all students.

Sincerely,

Lea Wenting Rysavy 22

Clarisse Hokia 23

Liza Hafner 21 M.S. 22

5-SURE on Foot student directors

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From the community | An open letter to the Stanford administration concerning alcohol culture on campus - The Stanford Daily

What Is Software-Defined Networking (SDN)? Definition, Architecture, and Applications – Toolbox

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Software-defined networking, commonly known as SDN, is defined as an architectural model for enterprise networks that enables them to be managed and optimized using a program-based approach. This article covers the definition, architecture, and applications of SDN.

Software-defined networking, commonly shortened to SDN, is an architectural model for enterprise networks that enables them to be managed and optimized using a program-based approach.

How SDN WorksSource: The METISfiles

Companies leverage SDN to unlink traffic management and network configuration processes from the physical network infrastructure. SDNs allow open application programming interfaces (APIs) to exert more granular control over organizational networks remotely. The SDN architecture offers enhanced flexibility for coordination between network devices that serve specific functions. As a result, it is rapidly gaining popularity among companies across industry verticals.

A report by the International Data Corporation (IDC) pegs the SDN controller software market at $1.2 billion in 2018 and forecasts it to reach $2.8 billion by 2023. IDC also forecasts the SD-WAN market to increase from $1.3 billion in 2018 to $5.2 billion by 2023. Further, a 2020 survey on global networking trends by Cisco found that more than 6 out of every 10 organizations surveyed had deployed controller-based automation in their data centers, while nearly the same number had adopted SD-WAN technologies.

A typical multinational corporation has thousands of devices that need to be programmed, monitored, and managed to ensure day-to-day business processes are carried out seamlessly. Traditional network operations involve configuring all these devices correctly, one by one, and then tracking the performance of every individual device. If this sounds overwhelming, thats because it actually is. Traditional network management is a tedious process that leaves room for errors and security breaches. Even network management systems do not completely eliminate performance issues, network bottlenecks, and other shortcomings.

SDN works by using algorithms to automate device configuration and management. SDN can scale operations according to evolving network requirements, regardless of the number of devices connected to the network. IT administrators can leverage SDN capabilities to program the network according to traffic flow patterns or other factors. Network programmability enables enterprises to optimize business operations at a global scale. With SDN, it becomes easy to achieve a consistent network-wide state that is otherwise impossible when each component is configured individually without the context of its surroundings.

SDN replaces segmented network operations with an optimized, logically centralized network state. Networks are no longer dependent on their underlying limitations, and management consoles can now be operated through APIs to maintain a consistent level of overall network functionality, performance, and control.

In the case of traditional network architectures, the manual configuration of devices is the norm. Hardware-based configuration makes it difficult to locate and fix faults, giving a single device the potential of having a domino effect on network performance.

A traditional business network is made up of three main architectural components:

Traditional networks operate using integrated control and data components. Physical network devices and their associated protocols and software need to be configured to make any changes to the network. Devices operate independently from each other and are only partially aware of the logic used by the other devices on the same network. Naturally, only limited changes are possible to the system at large due to the bottleneck created by having to manage individual network devices.

SDN unlinks the control and data components, thereby centralizing control over the network logic and enabling IT personnel to select the programmable features that need to be moved from the device level to the controller or application server. Organizations can leverage centralized network logic and delinked controller operations to enjoy enhanced agility levels by automating, monitoring, extending, managing, maintaining, troubleshooting, provisioning, and deprovisioning network infrastructure with ease.

Thanks to SDNs unlinked architecture model, applications can interact directly with the control layer and view the network state at the global level. This makes enterprise networks more scalable, flexible, and dynamic, thereby simplifying daily operations and allowing new business opportunities to be tested without having to worry about network architectural bottlenecks.

By enabling network programmability and unlinking network architecture components, SDN enhances vendor interoperability and network openness. This means traffic engineering, network integration, and inventory planning are easier. Device purchasing, commissioning, and management also become more straightforward. A more open, vendor-neutral enterprise network allows infrastructure investments into business and technical requirements to be optimized.

With remote work expected to remain popular in 2022, high data throughput, complicated network architectures, and ever-increasing demand for peak performance are expected to make traditional network management obsolete. This is because static traditional networks are often inadequate for fulfilling the dynamic demands of the post-COVID-19 business landscape.

Today, a robust digital presence is vital for an organization to thrive in its industry. This calls for business network infrastructure that is flexible enough to scale according to the needs of fast-evolving computing environments, cutting-edge technologies, and dynamic business landscapes.

See More: What Is a Content Delivery Network (CDN)? Definition, Architecture, and Best Practices

As outlined above, the SDN architecture decouples the control and data components of traditional networks. This allows companies to design networking and computing systems that use software-based technologies to optimize network hardware. Listed below are the key architectural components of SDN.

The application component consists of programs that communicate with the controller using APIs. This component transmits data about desired network behavior and required resources to the controller, creating an overview of the network status in the process. The application layer also collects data from the controller layer to make the required decisions for fulfilling application goals.

Examples of applications include analytics, networking management, and business processes for data center operations. For instance, an analytics application can be configured to bolster network security by recognizing suspicious activity.

The controller component receives requirements and instructions from the application component and uses logic to process and relay them to the networking layer. This core element of the SDN architecture enables centralized supervision and management, enforcement of network policies, and automation across both virtual and physical network environments.

The controller is also responsible for collecting data about network health and status from the hardware layer and communicating this information to the application component. This allows the application component to create an abstract network overview that includes statistics and events.

The datapath component allows users to supervise and exert control over the forwarding and processing of information by the hardware layer. This layer consists of a control-data-plane interface (CDPI) agent and a traffic-forwarding module and may also contain modules for network traffic processing.

A single network device can contain one or more SDN datapaths. Likewise, a single SDN datapath may be defined across multiple devices. This component can also help with processes such as management of shared hardware, logical to physical mapping, datapath slicing or virtualization, and compatibility with non-SDN networking.

The CDPI is used as an interface between the controller component and the datapath component. Its functions include allowing forwarding operations to be programmed, reporting network statistics, and notifying users of events of interest. Leading SDN solutions feature CDPI components that are open, interoperable, and vendor-neutral.

The NBI relays data between the controller component, the application component, and the policy layer. This component typically provides an abstract view of the network and enables the direct expression of network requirements and behavior, regardless of latitude (abstraction) and longitude (functionality).

The SBI relays data between the controller component and individual hardware units connected to the network, such as routers, access points, switches, and hardware firewalls. This component further classifies network concepts into more granular technical details meant for the lower layer of the architecture.

Simply put, SBIs enable network components to exchange data with lower-level components such as physical and virtual switches, routers, and nodes. For instance, routers rely on the SBI to view the network topology, decide network flow, and execute requests received from the NBI.

See More: What Is a Wide Area Network (WAN)? Definition, Types, Architecture, and Best Practices

Software-defined networking allows networks to be more flexible, agile, and manageable. Here is a list of the top 10 applications of SDNs in 2022.

Wireless internet is rapidly gaining popularity for advanced applications such as the industrial internet of things (IIoT), smart cities, autonomous vehicles, fleet management, and smart farming. This calls for a standard that allows for throughput at previously unheard-of speeds. To fulfill this need, the rapidly evolving telecommunications industry is moving toward 5G as the latest standard for cellular connectivity.

Since 2019, 5G technology is being deployed in a phased manner across the globe. However, the full potential of this technology has not yet been explored. Creating 5G infrastructure that relies on SDN is predicted to create speedy, open-source networks that will be a game-changer for the global telecom industry. SDN-augmented 5G networks are expected to help businesses solve connectivity issues, bolster network security, minimize latency at a competitive price, and enhance the overall user experience.

A software-defined mobile network integrates SDN with cloud computing and network function virtualization principles in mobile networking environments. SDMN is expected to drive the change from rigid and incompatible legacy mobile networks to dynamic and scalable connectivity planes. This would be achieved by separating the data and control planes, a core tenet of SDN. Essentially, SDMN is expected to manifest as an extension of the SDN paradigm, incorporated with functionalities specific to mobile networks.

As 2022 brings with it an exponential increase in new types of network-enabled devices, distributed applications, and their associated users, the complexity of connectivity architecture will increase. That is where intent-based networking comes in.

IBN is expected to transform manual, hardware-centric networks into controller-led environments that capture consumer intent and effectively distill it into policies that can be applied consistently across networks through automation. This would enable the network to monitor and tweak performance continuously to drive operations toward their desired outcomes.

IBN will manifest in the form of a closed-loop system that captures business intent and translates it into policies that the network can act on. These policies are then installed across both virtual and physical network infrastructure with the help of pan-network automation. Finally, machine learning and analytics are used to continuously monitor the network to verify the application of the appropriate intent and fulfill the desired business outcomes.

IBN will leverage SDN to implement a centralized control component for network activity. This will enable IT teams to view the whole network as an integrated entity. Holistic digital transformation would become far less challenging with controller-led networks across domains such as access, data centers, WAN, and cloud.

A software-defined wide area network is a form of virtual network architecture. Businesses leverage SD-WAN to securely connect applications and clients using any combination of LTE, MPLS, broadband internet, and other transport services. SD-WAN uses a centralized control component to securely direct traffic across the network to enhance application performance and user experience.

SD-WAN is managed by applying SDN principles. Decoupling the control and management components enables users to adopt commercially available leased lines to partially or completely replace MPLS lines, thereby minimizing costs. In fact, some estimates suggest that SD-WAN can be up to 2.5 times more economical than traditional wide area networks. Further, configuration and monitoring become easier as network administration is no longer tied to the hardware.

While SD-WAN has existed for some time, 2022 is expected to see relevant technologies packaged together in different ways. This is expected to drive new forms of network aggregation and management, as well as the dynamic sharing of network bandwidth across endpoints. As more businesses digitalize their operations to enhance agility and productivity, SD-WAN is expected to see an increase in popularity in the near future.

A software-defined local area leverages SDN principles in data center environments. This network architecture aims to enhance the flexibility, adaptability, scale, and cost-effectiveness of both wireless and wired access networks.

Businesses from across industry verticals are expected to continue operating online in 2022. For them, ensuring the continuity of network operations is critical. SD-LAN rises to the challenge by delinking hardware- and software-based network layers, thus creating an architecture driven by application and policy.

SD-LAN is expected to see an increase in demand in 2022 for its ability to create centrally managed local area networks that are self-organizing, easy to use, and simple to scale and integrate. Expected applications of SD-LAN in the near future include wireless connectivity without the need for a physical controller and the implementation of effective cloud management systems.

Minimizing latency and ensuring continuity is critical for running a successful online enterprise in the post-COVID-19 business world. One way to bolster redundancy measures and fight lag is to leverage distributed applications that operate across data centers. These applications replicate and synchronize data for load balancing, fault resiliency, and bringing information closer to users. This requires dependable delivery of data from one server to multiple clients. This model of data delivery is known as reliable group data delivery.

An increasing number of enterprises are expected to leverage SDN switches for RGDD in 2022. This is because SDN enables the implementation of rules that allow information to be forwarded to multiple ports simultaneously. For instance, a centralized controller can be set up to create forwarding trees that ensure robust RGDD while accounting for network congestion and load status.

In the dynamic corporate landscape of 2022, one slip-up is all it would take to lose a high-value client to a competitor. As such, a cutting-edge operational methodology is the need of the hour.

SDN addresses this concern by simplifying network control, making corporate networks more agile. This is achieved through direct programmability resulting from the separation of forwarding functions. SDN also enhances organizational agility by extending the ability of a business to use dynamic load balancing. This helps better manage traffic flow according to fluctuations in need and usage.

Simply put, SDN minimizes latency and enhances overall network efficiency.

The trend of remote work is expected to continue well into 2022. This calls for simplification of network configuration operations for a wide array of circumstances, including new employee onboarding, new office setup, and extension of connectivity for clients.

SDN speeds up these processes by allowing users to write automated programs that can configure, optimize, and secure network resources as required. Further, this architecture helps simplify the designing and operation of networks by replacing vendor-specific protocols with open controllers.

SDN also enables the use of micro-segmentation to minimize network complexity. Finally, consistency is established across network architectures such as private cloud, public cloud, hybrid cloud, and multicloud with the help of SDN.

As companies recalculate their attack surfaces due to new vulnerabilities arising from remote work, cybersecurity is expected to be of utmost importance in 2022. SDN assists in bolstering the security posture of companies by offering centralized and granular control over network security. For instance, SDN allows network administrators to implement policies for different network segments and workload types from a central location.

Further use cases for SDN in cybersecurity include detecting and mitigating DDoS attacks, countering worm propagation, and preventing botnet attacks. SDN controllers can also be used to help implement moving target defense (MTD) algorithms, which periodically hide or change the key properties of networks to minimize their attack surface.

In the post-pandemic business world, a strong network defines any company, regardless of the industry. With the manufacturing sector picking up steam once again, there is an increasing need to connect manufacturers with suppliers and distributors to ensure smooth operations and maximize profitability. As a result, smart manufacturing and plant solutions have become more popular than ever.

SDN architecture helps connect numerous categories of endpoints in manufacturing plants, including IoT devices, sensors, and smart machines. The enhanced flexibility and efficient operability offered by SDN are bound to make it popular in the manufacturing space in 2022 and the years ahead.

See More: Wide Area Network (WAN) vs. Local Area Network (LAN): Key Differences and Similarities

Software-defined networking has several applications in the post-pandemic corporate landscape. Its adaptability, ease of implementation, and automation-friendly architecture are expected to help SDN see heightened popularity in 2022 and beyond.

Further, with an ever-increasing number of companies adopting cloud platforms, SDN is expected to help enable the virtualization of cloud-enabled networking infrastructure. The responsiveness, automation capabilities, and cybersecurity applications of this emerging technology are only expected to increase in the near future.

SDN technology is the key for enterprises looking to solve networking challenges such as latency, geo-boundaries, and bottlenecks.

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What Is Software-Defined Networking (SDN)? Definition, Architecture, and Applications - Toolbox

Metas Biggest Encrypted Messaging Mistake Was Its Promise – WIRED

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Since the 1990s, governments around the world have often used the welfare of children as an excuse for all kinds of internet policy overreach: encryption backdoors, centralized censorship mechanisms, and anti-anonymity measures. So when Meta, facing pressure from the government as well as NGOs, announced its decision last week to delay the rollout of end-to-end encryption for messaging systems such as Instagram DMs and Messengerwith child safety as the cited reasonprivacy advocates were understandably upset and suspicious. But speaking as someone who previously worked on safety and security at Facebook, I dont view the delay as an arbitrary political decision. The concern over the safety of young users is genuine, and the problems are pervasive, especially when it comes to social systems as complex as those at Meta.

Frustrating as it may be, the companys delay is likely justified. Some form of end-to-end encryption should be available to all people, to preserve the right to private communication and prevent government incursions. But end-to-end encryption isn't just one issue or technologyits a broad set of policy decisions and use cases with high-stakes consequences. As such, creating the proper environment for its use is a complex task. The need for end-to-end encryption, as well as the conditions required to implement it safely, vary for each platform, and apps like Facebook and Instagram still require serious changes before it can be introduced without compromising functionality or introducing safety risks. Metas greatest misstep isnt this latest delay but rather the timeline, and perhaps even the outcome it promised.

When then-Facebook first announced its timeline to implement interoperable end-to-end encryption across all its properties in 2019, its immediate infeasibility was clear. The proposed timeline was so rapid that even producing the technology itself would be nigh impossible, with safety mechanisms barely entering the picture. Systems like WhatsApp already had end-to-end encryption and content-oblivious mechanisms for detecting some kinds of harm, and it was assumed this would readily translate to other Facebook properties.

However, apps and sites like Facebook and Instagram are wildly different in architecture and dynamics than WhatsApp. Both implement direct messaging alongside systems that attempt to actively connect you with people, derived from a combination of reading users' phone books, algorithmically determining similar accounts based on locations, interests, and friends, as well as general online activity. In the case of Facebook, large public or private groups also facilitate expansion of one's social graph, along with global search of all accounts and grouping by institutions such as schools. While apps like WhatsApp and Signal operate more like private direct messaging between known contacts, Facebook and Instagrams growth-oriented design leads to situations where abusers can more easily find new victims, identities and relationships are accidentally exposed, and large numbers of strangers are mixed together.

These fundamental differences mean that before Meta can safely switch all of its platforms to end-to-end encryption, its apps must undergo some nontrivial changes. First off, the company must improve its existing content-oblivious harm-reduction mechanisms. This involves using social graphs to detect users who are trying to rapidly expand their networks or to target people of certain demographics (for example, people of a particular declared or inferred age), and finding other potentially problematic patterns in metadata. These mechanisms can work hand in hand with user reporting options and proactive messaging, such that users are presented with safety messaging that informs them of their options for reporting abuse, along with efficient reporting flows to allow them to escalate to the operator of the platform. While these types of features are beneficial with or without end-to-end encryption, they become significantly more important when the ability to inspect content is removed.

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Metas Biggest Encrypted Messaging Mistake Was Its Promise - WIRED

Global Hardware Encryption Market Trends and Opportunities to 2030 | Western Digital, Samsung Electronics, and Micron Technology among others – Taiwan…

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The global hardware encryption market size was US$131.3 billion in 2019. The global hardware encryption market size is forecast to reach the value of US$2,277 billion by 2030 by registering a compound annual growth rate (CAGR) of 31.1% during the forecast period from 2021-2030.

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COVID-19 Impact Analysis

The COVID-19 epidemic had a nominal effect on the global hardware encryption market. During the pandemic, encryption became a crucial tool in tracking crises. Moreover, the organization began adopting various methods to protect the data. Apart from that, government bodies adopted hardware encryption to control the situation securely. As a result, it propelled the growth of the global hardware encryption market.

Factors Influencing

Geographic Analysis

Asia-Pacific is forecast to dominate the market during the forecast period from 2021-2030. It is due to the growing urbanization, rise in household income, and rapidly growing population in emerging countries, such as China and India. Moreover, the availability of electronic and semiconductor manufacturing companies would significantly contribute to the market growth during the analysis period.

North America and Europe may register strong growth rates because of the rising era of cloud-based services and innovations in the Internet of Things (IoT) based technologies. Moreover, these regions are home to many established hardware encryptions manufacturing firms. Therefore, the market would witness various growth opportunities in these regions.

Competitors in the Market

Market Segmentation

Insight by Architecture Type

Insight by Product Type

Insight by Algorithm & Standard

Insight by Application

Insight by End-User

Insight by Region

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How the 5G industrial IoT will change different verticals – IT Brief New Zealand

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The industrial Internet of Things, protecting systems during the holiday season, and using encryption to help address cloud misconfiguration, are amongst the most recent insights from global technology firm Thales.

Industrial IoT

The industrial Internet of Things is coming and many believe it will be the catalyst for unprecedented productivity levels.

However, according to Thales Group, it cannot happen without fast and reliable connectivity extending to even the remotest locations.

"5G provides it. And it is already here," the company says.

5G networks are now rolling out across the world. According to the GSA, there were 180 commercial services in 72 countries in September 2021. Meanwhile, 465 operators in 139 markets are planning live deployments.

"So, the conditions are in place for the Industrial IoT to flourish," Thales says. "Indeed, the first pioneering services have been built."

How to protect your systems from unauthorised access this holiday Season

Many retailers and shipping services are planning to hire temporary workers for the upcoming holiday season. Retailers, shipping and logistics companies, and other organisations can take multiple steps to mitigate the risks posed by seasonal workers by ensuring they have the right access security in place.

How encryption can help address Cloud misconfiguration

Cloud service providers (CSPs) try to make it simple and easy for their users to comply with data privacy regulations and mandates. Still, as all of those who work in technology know, you reduce access to granular controls when you simplify a process. On the flip side, if you allow access to granular controls, the person setting the controls needs to be an expert to set them correctly. And, even experts make mistakes.

New partnership between Thales and VNPT to accelerate digital transformation in Vietnam

Thales and the Vietnam Posts and Telecommunications Group have signed a Memorandum of Understanding (MoU) to explore technical collaborations in telecommunications satellites, Smart and Safe cities, Digital Identity and Biometrics, 5G & Internet of Things and cybersecurity.

The MoU was signed between both organisations on 3 November 2021 in Paris, in the presence of Jean Castex, Prime Minister of France, and Pham Minh Chinh, Prime Minister of the Socialist Republic of Vietnam. The joint collaboration on these topics aligns with Vietnam's national digital transformation goals, including establishing a strong digital economy and driving towards a Smart Nation.

Thales is a global technology company investing in digital and "deep tech" innovations, including Big Data, AI, connectivity, cybersecurity and quantum technology.

"Thales's core purpose is to build a future we can all trust. It's the exact transcription of the DNA that has shaped the Group ever since it was founded more than a century ago," the company says.

It has more than 80,000 employees across five continents, and its corporate purpose revolves around three components: autonomy, resilience and sustainability.

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How the 5G industrial IoT will change different verticals - IT Brief New Zealand

AWS re:Invent: How to Use Machine Learning and Other Technology to Make the Most of Your Data – Inc.

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If your company isn't treating data like an asset, youcould bemissing out ona majorgrowth opportunity.

That's according to SwamiSivasubramanian, vice president of Amazon Machine Learning.Sivasubramanianwas speaking duringa keynote conversation ondata and machine learning WednesdayatAWS re:Invent,a conference forbusiness owners and other technical decision-makers hosted byAmazon Web Services in Las Vegas.

Sivasubramanian says there are three thingscompaniescan do to make the most of their data. Here's his advice.

1.Modernize your data infrastructure.

Too many companies still treat their data like it's the 1990s when they should be implementing a modern data strategy, according to Sivasubramanian.This applies to both storing your data and"putting your data to work," he says. In many cases,hiring an outside company tomanage your databasefor you can save resources and help ensure your operations run smoothly. Sivasubramanian adds that acloud-basedsolution will help ensure that your company'sdata--even the most obscure, infrequently used bits--can be easily accessed by your teams that need it.

Applying modern solutions like machine learningto your database can alsohelp you detect problems faster. For example, an applicationslowdown that might otherwise go undetected for dayscan be identified and diagnosed quickly with machine learning. It can also provide suggestions for fixing problems with your data,which can be time consuming and costly if you're still doing somanually.

2.Unify your data.

It's important to have whatSivasubramanian refers to as a"single source of truth" about your business. Ensuring that your teams are all looking at the same data can help your company make the most of it. Of course,this doesn't mean every teamshould have access to every piece of data; different teams can and should have different permissions and levels of access. What's important is that this data is consistently reported and recorded.

"Opportunities to transform your business with data exist all along the value chain," says Sivasubramanian."But creating such a solution requires companies to have a full picture and a single view of their customers and their business."

3.Find innovative uses foryour data.

Applying insights to your data can help you improve existing operationsor build entirely new ones.Sivasubramanian pointsto severalAWS customers that have benefited from applying machine learning and analytics to their data.Tyson Foods has usedcameras armed with computer vision to identify ways to reducewaste bycutting down on packaging. AndPinterest has usednatural language processing to create more accurate search engines that allow employees to find the information they need faster.

"Machine learning is improving customer experiences, creating more efficiencies, and spurring completely new innovations," saysSivasubramanian. "And having the right data strategy is the key to these innovations."

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AWS re:Invent: How to Use Machine Learning and Other Technology to Make the Most of Your Data - Inc.

The Beatles: Get Back Used High-Tech Machine Learning To Restore The Audio – /Film

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"TheBeatles: Get Back" is eight hours of carefully curated audio and footage from The Beatles in the studio and performing a rooftop concert in London in 1969. Jackson had to dig through 60 hours of vintage film footage and around 150 hours of audio recordings in order to put together his three-part documentary. Once he decided which footage and audio to include, then he had to take the next difficult step: cleaning up and restoring them both to give fans a look at TheBeatles like they had never seen them before.

In order to clean up the audio for "Get Back," Jackson employed algorithm technology to teach computers what different instruments and voices sounded like so they could isolate each track:

Once each track was isolated, sound mixers could then adjust volume levels individually to help with sound quality and clarity. The isolated tracks also make it much easier to remove noise from the audio tracks, like background sounds or the electronic hum of older recording equipment. This ability to fine-tune every aspect of the audio allowedJackson to make it sound like theFab Four are hanging out in your living room. When that technology is used for their musical performances, it's all the more impressive, as their rooftop concert feels as close to the real thing as you can possibly get.

Check out "TheBeatles: Get Back," streaming on Disney+.

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The Beatles: Get Back Used High-Tech Machine Learning To Restore The Audio - /Film

A machine learning pipeline revealing heterogeneous responses to drug perturbations on vascular smooth muscle cell spheroid morphology and formation |…

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2021 AI Predictions: What We Got Right And Wrong – Forbes

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DeepMind CEO Demis Hassabis had a big 2021.

In December 2020, we published a list of 10 predictions about the world of artificial intelligence in the year 2021.

With 2021 now coming to a close, lets revisit these predictions to see how things actually played out. There is much to learn from these retrospectives about the state and trajectory of AI today.

Outcome: Wrong

As of the beginning of this year, no autonomous vehicle company had ever gone public. 2021 is the year that that all changed.

TuSimple, Embark and Aurora have all debuted on public markets this year. Argo is deep in preparations to go public. Plus.ai and Pony.ai both announced SPAC deals this year (though Pony.ai has since shelved its plans). Credible rumors are swirling about upcoming public market debuts for other autonomous players.

But Waymo and Cruise are not included on that list.

Given that Waymo and Cruise are the most well-capitalized of all AV companies, it makes sense that they would not necessarily be the first ones to need to tap public markets for more capital.

Still, while our timing proved premature, we expect both of these companies to eventually be publicly traded.

Outcome: Wrong

Deepfakes, which just a couple years ago were an oddity on the fringes of the Internet, have thrust themselves into mainstream public consciousness in 2021.

From an Anthony Bourdain documentary to viral Tom Cruise clips, from a widely condemned new pornography app to a bizarre story about a cheerleaders vindictive mom in small-town America, deepfakes are rapidly becoming a part of our societal milieu.

But no deepfake has yet fooled large numbers of viewers and caused meaningful real-world damage in the realm of U.S. politics. Lets hope it stays that way in 2022.

Outcome: Right(ish)

Research activity in federated learning has indeed surged this year.

The number of academic research papers published on federated learning grew from 254 in 2018, to 1,340 in 2019, to 3,940 in 2020, according to Google Scholar. In 2021 that number jumped to 9,110, with four weeks still left in the year.

In last years predictions we specified that this number would surpass 10,000 in 2021hence the ish. This one may come down to the wire....

Outcome: Wrong

No multi-billion-dollar acquisitions occurred in the world of AI chips in 2021.

Instead, the leading AI chip startups all raised rounds at multi-billion-dollar valuations, making clear that they aspire not to get acquired but to become large standalone public companies.

In our predictions last December, we identified three startups in particular as likely acquisition targets. Of these: SambaNova raised a $670 million Series D at a $5 billion valuation in April; Cerebras raised a $250 million Series F at a $4 billion valuation last month; and Graphcore raised $220 million at a valuation close to $3 billion amid rumors of an upcoming IPO.

Other top AI chip startups like Groq and Untether AI also raised big funding rounds in 2021.

Outcome: Wrong

In 2021, none of the leading AI drug discovery startups was acquired by a pharma incumbent. Instead, just like the AI chip startups in the previous section, these companies raised record amounts of funding to challenge the incumbents head-on.

Several AI drug discovery players completed IPOs in 2021, making them among the earliest AI-first companies in the world to trade on public markets.

Recursion went public in April; Exscientia followed it in October. Insilico is slated to IPO soon. Insitro, XtalPi and a handful of other AI drug discovery players raised massive private rounds this year. For most of these competitors, the window for an acquisition has likely passed.

Outcome: Right

Finally, a prediction that we nailed!

For years, U.S. policymakers have been relatively inattentive to the strategic importance of artificial intelligence while more forward-thinking governments like China and Canada have rolled out detailed national strategies to position themselves as global AI leaders.

This changed in a big way in 2021, with an explosion of U.S. public policy activity related to AI. At the beginning of the year, Congress passed legislation to promote and coordinate AI research. Numerous additional AI-related bills have been introduced in both chambers of Congress this year. A dedicated White House group has been established to oversee the nations overall approach to AI. The U.S. military has gone into overdrive in its AI investments. In October, the Biden administration called for an AI Bill Of Rights for the American people. The list goes on.

It would be going too far to say that the U.S. government has established a cohesive national AI strategy. But in 2021, artificial intelligence rocketed to the forefront of Washingtons policy agenda.

Outcome: Right

In January 2021, less than a month after we published our predictions, Google announced that it had trained a model with 1.6 trillion parameters, making it the largest AI model ever built.

Now the question ishow big will these models get in 2022?

Outcome: Right(ish)

The crowded MLOps landscape has begun to consolidate in 2021. In several instances this year, large AI platforms have acquired smaller startups building tools and infrastructure for machine learning.

Probably the most noteworthy example came in July with DataRobots acquisition of Algorithmia, which had raised close to $40 million in venture capital funding.

Other examples include HPEs acquisition of Determined AI and DataRobots acquisition of decision.ai.

But there was less M&A activity in MLOps this year than we expected. In last years predictions, we listed 14 MLOps startups that we saw as potential acquisition targets. Of these, only oneAlgorithmiaended up being acquired. (Several others on that listWeights & Biases, Snorkel AI, OctoMLinstead raised rounds at monster valuations.)

Outcome: Right

Regulatory momentum for antitrust action against Big Tech has been building for years given the outsize influence that companies like Alphabet, Amazon and Facebook exert over the economy. But over the past year, antitrust regulators have increasingly refined their messaging by focusing on the structural advantages that these giants enjoy in AI. The jumping-off point, almost always, is the companies unrivaled data assets and aggressive data accumulation practices.

From recent Senate antitrust hearings to presidential Executive Orders, this theme of unfair data advantages translating into unfair AI advantages is becoming an increasingly important dimension of the Big Tech antitrust movement.

Last month, for instance, Lina Khans Federal Trade Commission appointed prominent AI critic Meredith Whittaker to a special role as the FTCs senior adviser on AI. As one industry observer put it: Whittaker's hiring is just the latest evidence of the FTCs attention on algorithms and algorithmic issues.

Outcome: Right

Of the predictions on last years list, this one was the most open-ended and least verifiable. Even so, plenty of developments in 2021 point to the continued emergence of biology as the most important and high-impact of all AI application areas.

AI is transforming drug discovery, with profound implications for the pharmaceutical industry and the future of human health. AI-discovered therapeutics are now in clinic; AI drug discovery startups are now trading on public markets.

DeepMinds landmark AlphaFold work, which was published in July, is a testament to the almost magical potential for machine learning to uncover fundamental truths about how life works. We have previously argued in this column that AlphaFold is the most important achievement in the history of AI. As Alphabets big announcement about Isomorphic Labs last month underscores, AlphaFold is just the beginning.

Perhaps more so than any other area of AI, world-class talent and investment dollars are flooding into computational biology. Take, for example, Eric Schmidts $150 million donation earlier this year to establish a new center at Harvard and MIT that will catalyze a new scientific discipline at the intersection of biology and machine learning.

In the years ahead, the application of computational methods and machine learning to biology is poised to transform societyand perhaps life as we know it.

Note: The author is a Partner at Radical Ventures, which is an investor in Untether AI.

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2021 AI Predictions: What We Got Right And Wrong - Forbes

Announcing the Intelligent Applications Top 40; a New Industry Ranking of the Top Private Companies Building Applications with AI and Machine Learning…

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SEATTLE--(BUSINESS WIRE)--Today the Intelligent Applications Top 40 (#IA40) was unveiled by Madrona Venture Group. The list of the top private companies building applications that truly incorporate intelligence into how they process data and predict outcomes was voted on by the top 40 venture capital firms investing in this industry, Goldman Sachs, Amazon and Microsoft. http://www.ia40.com

The era of SaaS applications is drawing to a close as their intelligent application counterparts rise to prominence. We have been investing in intelligent apps since 2011 and we believe that in 2022, we will see a significant shift in how companies build applications, deliver insights and change our lives and these 40 companies will be driving that shift, commented Matt McIlwain, Managing Director, Madrona Venture Group. We are honored to partner with Goldman Sachs and all the top tier venture firms who contributed to this ranking of the companies that are building the future of software.

To create this list, Madrona Venture Group and Goldman Sachs collaborated on a new initiative to research, identify and select the top intelligent application companies in the private sector. The IA40 initiative enlisted judges across 40 of the most active venture capital firms to participate in this research-driven ranking. Judges nominated over 250 companies that they believe will transform and define the next generation of software with application intelligence, and then voted for the top 40 most promising.

The top 40 companies were selected across categories

Early

Mid

Growth

Enablers

Runway ML

Abnormal Security

Gong

dbt labs

Tesorio

Instabase

Snyk

Databricks

Spot AI

Loom

Samsara

Hugging Face

Auditoria

Amperity

Cribl

Cockroach Labs

TruEra

Primer.ai

Starburst Dat

DataRobot

Clockwise

Hyperscience

Celonis

Fivetran

WellSaid Labs

Axonius

Anduril

OctoML

LinearB

Cresta.ai

Chainalysis

Grafana Labs

Machinify

Seekout

Workato

Monte Carlo

Replicant

Moveworks

Snorkle AI

Synthesia

Top Line Insights

Insights

We thank all the judges of the IA 40.

a16z, Accel, Acrew Capital, Addition, Allen Institute of AI, Altimeter, Amazon, Amplify, Bain Capital Ventures, Battery Ventures, Bessemer Venture Partners, Coatue , CRV, Decibel Partners, defy.vc, Emergence Capital, General Catalyst, GGV Capital, Goldman Sachs, Greylock, ICONIQ Capital, Insight Partners, Khosla Ventures, Kleiner Perkins, Lux Capital, M12, Madrona Venture Group, March Capital, Mayfield Fund, Meritech Capital Partners, NEA, Norwest Venture Partners, OpenView, Positive Sum, Redpoint, Revolution Ventures, Scale Venture Partners, Sequoia Capital, STEADFAST Capital, Two Sigma Ventures.

Intelligent Application Definition

Intelligent applications leverage machine learning models embedded in applications that use both historical and real- time data to build a continuous learning system. These learning systems solve a business problem in a contextually relevant way - better than before, and typically deliver rich information and insights that are either applied automatically or leveraged by end users to make superior decisions.

About Madrona Venture Group

Madrona (www.madrona.com) is a venture capital firm based in Seattle, WA. With more than 25 years of investing in early stage technology companies, the firm has worked with founders from Day One to help build their company for the long run. Madrona invests predominantly in seed and Series A rounds across the information technology spectrum and has also raised Acceleration Stage funds for initial investments in Series B, C and beyond. Madrona was an early investor in companies such as Amazon, Smartsheet, Rover, Redfin, and Snowflake.

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Announcing the Intelligent Applications Top 40; a New Industry Ranking of the Top Private Companies Building Applications with AI and Machine Learning...


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