The Corruption of the Democratic Party: Talking to Ted Rall about his new book – CounterPunch

Seven Stories Press just released Ted Ralls new book,Political Suicide: The Fight for the Soul of the Democratic Party.Rall is a graphic novelist, a syndicated columnist and the author of many books of art and prose, including biographies of Edward Snowden, Bernie Sanders and Pope Francis. Youve probably seen his political cartoons, which are often published in urban weeklies.

Political Suicide uses the graphic novel form to trace the history of the Democratic Partys rightward movement over the last few decades, and how its leadership has worked to suppress the partys progressive wing.

Ted & I talked on June 27thfor my podcast, Voice for Nature & Peace.You can listen to the full conversation here. What follows are two extended excerpts, edited for clarity.

Kollibri:Theres often been a discussion about the Democrats: How comethey dont win as much as they should? How come people like Trump are able to win? It often comes down to this argument of, Is it incompetence or is it corruption? And your book comes down pretty solidly on the side of corruption.

Ted:Yes. The question is: Is it a plan? Is it a conspiracy or is it a system? I think its more a system that ends up creating systemic corruption. Thats my conclusion. Obviously, its impossible toknowwithout being a fly on the wall in the room where it happened. But, since were not, you kind of have to draw the conclusion that these are people who know what theyre doing, but they dont know why.

I think they sort of have articles of religious faith within the DNC. For example, the whole centrist/triangulation thing. You know: If you go too far to the left you just cant win. But theres no real data to support that it. It may be true but [they need to] prove it. They havent been winning much with their current approach. This is not a party that sweeps a lot of elections.

There are more registered Democrats than registered Republicans. So by definition, if everybody votes in roughly equal numbers, Democrats should win most of the time. But they dont, so obviously whats happening is that Democrats are less motivated to vote than Republicans. So the question is, Why is that?

Are people who are left of center intrinsically more apathetic of lazier or less likely to vote when its drizzling on a cold day in November? Or, are they just simply less excited about their candidates and feel that less is at stake. We can argue about this, but obviously you know where I come down on this point: I think theyre just less excited.

You know that, for example, if Joe Biden is elected, youre not going to have an exciting new policy agenda thats really going to thrill us. Where, if say, Bernie Sanders or even Elizabeth Warren had been [nominated], youd know there would be a possibility that some exciting policies would be at least proposed and fought for, if not necessarily enacted.

Kollibri:It seems to me that with Biden, this is the toughest case theyve made for themselves in years that theyre the lesser of the two evils.

Ted:Yes. No doubt. Whats funny about this is that Biden is asking, essentially, for a blank check. Its not very likely that hell even be alive in four years. So, we dont even really know who the president will be because itll be the vice president. But we dont know who his vice presidential pick is. Yet were being asked to support this future, unknown president.

Also, the country effectively will be run by the cabinet and a shadow cabinet of DNC power-brokers. And we dont know who any of those people are either. Literally, its, Vote for this unknown cabal. All we can tell you is that theyre not Donald Trump. We will also tell you that on all the key issues that progressives currently care aboutwhether its defunding the police: Biden says hes against that; the Green New Deal: Biden says hes against that too, cant afford it; or Medicare-For-All: Biden says hes against that, too; student-loan forgiveness: on that, Biden has been downright Scrooge-like, which is especially weird. Its like the COVID-19 pandemic hasnt changed his thinking on anything. Youd think that with the economy in the toilet, youd say, Well, its too much to ask for a country with at least 25% unemployment to pay back their student loans. Or maybe its a lot to ask people in the age of COVID to go work for less than $15 an hour. Or maybe Medicare-For-All isnt even as much as we need because people are literally not going to the doctor because people feel like they cant afford it, and the pandemic shows the insanity of that.

But he hasnt changed any of it

I was thinking about framing. The sales pitch for each candidate: we all know it. Hillarys sales pitch was, I have an awesome resume, Im really experienced, Im very qualified. Donald Trumps sales pitch was, America has become a shit-hole country. Our infrastructure is falling apart. The streets of our Midwestern Rust Belt cities are crumbling. I will make this country the way it looked in the 1950s during the post-War expansion, and also, incidentally, white males will be back in charge. It will look like the 1950s again. We understand the sales pitch: Make America great again.

But with Biden, the sales pitch is: A return to normalcy. Quote-end-quote, a return to Obama-era normalcy. The problem with that it is two-fold. One, Obama wasnt that great. Things werent that great under Obama. But I think the bigger problem with the sales pitch is, this isnt really normalcy. It isnt normalcy to have a president who is clearly mentally decomposing before our eyes.

The last two years of Woodrow Wilson, the last two years of Eisenhower, the last years of Ronald Reagan: were all presidents who were mentally impaired in some way. But the thing is, they werent elected that way. Here were being asked to literally vote for a guy who tells us, Im not that sharp. Thats why Im only going to be a one-term president. Theres this implication: Im going to have all these awesome people running the show behind the scenes. Im going to have my own team of best and brightest. Parenthetically, theres no evidence to support that because he wont tell us who they are. I assume theyll just be a bunch of Obama-era hacks because those are the people he knows. They werent great either.

So the sales pitch doesnt work, because weve never been asked to vote for, basically, a president whose already mentally impaired out of the gate, and that everythings going to be run by a shadow government that we dont even know. Thats never been something the American people have been asked to sign up for. Its not really normalcy. Its something else

Kollibri:A lot of your bookto turn to how we got herecovers the recent history (and further back than that) of the Democratic Party, showing the patterns here, and showing how theyve presented themselves as one party but theyve been something else, basically the entire time. I think that a lot of this history is really whats valuable about your book, and what people wouldnt ve have known before.

For example, I was fascinated particularly by the section on Jimmy Carter, because hes presented as such a saint these days, and yet he did have a very checkered history in that office, and he was the beginning of the rightward lurch.

Ted:Yes. Carter is super old and obviously could dieand will die soonand when he does, just watch how hes a lionized as a hero of American liberalism. But thats bullshit. He absolutely was the beginning of the whole Democratic southern strategy which Clinton followed Carter definitely ran as a moderate and governed as a right-wing Democrat. People forget that he brought back draft registration. He funded the Mujahideen, who ultimately morphed into Al-Qaeda in Afghanistan against the Soviets. Afghanistan looks the way it does now because he listened to Brzezinski.

The Reagan defense build-up of the 1980swe call it the Reagan build-upbut it really began in 1978 under Jimmy Carter and just continued under Reagan.

Carter was a hawk. In fact his policy with Iran was hawkish enough to provoke the hostage crisis. It didnt just befall him. It was something he brought on himself by propping up the Shah, and inviting the Shah to come to the United States to seek medical care.

Any progressive historian has to look at Jimmy Carter and say, This guy is the beginning of the end of the Democratic Party as a party that represents working class people.

Kollibri:Then just a few years later, we had Jesse Jackson running in sort of a similar role as Sanders was the last two years, coming from the progressive left. Jackson was the first candidate I supported. 1988 was the year I turned 18 and I was able to caucus for Jesse Jackson where I was going to college that year. I remember looking back at Jesse Jacksons platform a couple years back and being like, Wow, this is almost unrecognizable as being a Democratic Party platform at this point.

Ted:Jesse Jacksons achievement was remarkable. His foreign and domestic policy agenda was far to the left of anything that even Bernie Sanders could contemplate today. He ran twice84 and 8888 was the bigger run. I was 25 in that election and I voted for him too. I think its been forgotten to history because the Democratic Party and their media allies have covered it up.

But he won a lot of primaries. He really well against Dukakis. He absolutely gave the Democratic Party a major run for its money. He did better than anyone expected that he could havepossibly including himself! After that primary run, you have to look back and say, You know, he may not have necessarily been ahead of his time. He may not have realized that it reallywashis time. But then, thats all been swept under the rug ever since and the Democratic Party really didnt want to have anything to do with him.

Kollibri:No. One of the ways his platform really stood out [was] he was actually calling for defense cuts. I recall that the Democrats used to be the party that would call for defense cuts but I believe that stopped with Clinton.

Ted:Yeah, it did. And Clinton definitely helped move the needle to the right. Look, were talking about a party that has not proposed defense cuts in many decades. It also hasnt proposed an anti-poverty program since the 1960s. Literally. I use the word, proposed very carefully. In other words, its not like Obama or Clinton ever put forward a bill and then the Republicansthe big bad Republicanskilled it. No, they never even asked for it. It obviously, clearly was never a concern of the Democratic Party, or the White House, at all. They didnt care. They didnt even want to put themselves on the record as saying, This is something that ought to be something we care about in this country.

Kollibri:Right. So at this point, its basically just a hollow reputation that the Democrats are just coasting on.

Ted:I believe so, yes. It reminds me a lot of how people come from all over the world to the United States, to a country that they think the streets are paved with gold, and Im always like, You know guys, were just coating on our old rep. But it aint true. This country is just not that great. When you come here as an immigrant, its not much fun.

The Democratic Party is very similar. Theyre still coasting on the reputation created by FDR, and to a lesser extent I would say, by LBJ. And thats pretty much it. LBJ was the last president who really made fighting poverty and income inequality any kind of priority.

Kollibri:So now were in this place where: Wheres a progressive supposed to go? And maybe there just isnt any place for a progressive to go within the electoral arena.

Ted:Theres the Green Party. People can say thats not viable. Well, its not viable because people choose to not vote for it. Obviously, both major parties at one time were minor parties and then people donated money to them and voted for them and they became bigger. At some point, people had to be willing to quote-end-quote waste their vote in order to change the existing dynamic, like in say 1832 or 1856 or 1860.

The same thing is true now. If you want a party like the Greens or a new partyIve been advocating for a new progressive partybut if you want that to happen, youre going to have to vote for them and create it. Youre going to have to be willing to quote-end-quote waste your vote.

But I think the truth is that the system is set up to keep third parties from having ballot access. The die is caste. The game is fixed. Its very very difficult to get any traction because the duopoly really controls everything. The one thing they can agree onwell, they can agree on lots of things, unfortunatelyis that they dont want any third party to gain any traction.

Kollibri:They work very hard at that at the state level. Also, the fact that the debates were taken over by the two parties. That made a huge difference too.

Ted:Thats something that went almost unnoticed. It used to befor those of us, like you and me, who are old enough to rememberpresidential debates were always sponsored by the League of Women Voters. Now, its called like the Commission on Presidential Electionsor something like thatand that thing is run by the two major parties.

Its not a coincidence that the last time there was a third party candidatelike say John Anderson in 1980 or Ross Perot in 1992it was under the auspices of the League of Women Voters. But ever since the two parties have run it, theyve managed to keep people like Ralph Nader off the debate stage, but I think that does a tremendous disservice to democracy. We need as many choices as possible.

It shouldnt just be a major third party candidate like Ralph Nader, but Id like to hear from the Socialist Workers. In other countries, the smaller parties are taken a lot more seriously by the media, and are given more of a voice, so quote-end-quote fringe or smaller constituencies have a voice in the system and thats part of the reasonin my view, its the main reasonwhy voter turnout in other countries is so much higher

Kollibri:So it comes back around to that Howard Zinn quote again, about how its not important whos sitting in office, but whos sitting in the streets, and sitting in the lunchrooms and all that.

Ted:Thats right. Theres this famous poster from the May 1968 uprising in Paris of a woman throwing a brick at the viewer and it says, Beauty is in the streets. What that means is that real politics is in the street, its protesting. We see that right now with Black Lives Matter.

Until the pandemic, protesting was something that people didespecially white peopledid on weekends as a getaway to Washington or to their state capitol, and you went and walked around and you chanted and you felt good about yourself and then you went home and got ready for the work week. Now, with one in four voters unemployed because of the pandemicsitting at home, nothing to do, no distractions, no sportsreally theres nothing to do but protest or stay at home. Suddenly were in an era of permanent protest and the power of that is just amazing. We have a long long way to go, but I thought we were never going to get rid of those stupid Confederate statues or those stupid Confederate flags. Suddenly, Oh, we dont need those anymore. And the difference is protest every single day on an ongoing basis. Thats all the difference in the world. Thats what the 60s were like. We havent seen that since the 60s, where protest is everywhere. Its not just a big splash in Washington on May Day and then we all go home. Its every single day and its in Dayton, Ohio, and its in Lansing, Michigan, and its everywhere.

Thats where politics lives: outside in the streets.

Full interview can be found here.

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The Corruption of the Democratic Party: Talking to Ted Rall about his new book - CounterPunch

Everyones talking about TikTok but 100 million Indian smartphone users are missing this app – ThePrint

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New Delhi: UC Browser was Indias second most popular mobile browser before the government banned Chinese applications on 29 June.

An estimated 100 million Indians, who had the the application downloaded on their phones, now stand to lose access to the internet.

UC Browser is a Chinese mobile browser run by UC Web, a company owned by tech giant Alibaba which acquired the firm in June 2014. The companys revenue, reported at $571 million dollars in 2017, will likely be impacted by the ban given that India is a key market for the company.

Pavel Naiya, a senior analyst with analyst firm Counterpoint Technology Market Research, said as many as 100 million smartphone users had UC browser installed on their mobile devices before the ban, according to the firms estimates.

The browser was more popular among users of budget phones, devices priced at Rs 10,000 or below, Naiya said.

Also read: Banning apps violates WTO rules, will affect employment of Indians: Chinese embassy

A 2015 IANS report stated UC Browser was Indias third most-used app after Facebook and WhatsApp. Indian users spend five million more monthly hours on UC Browser than on Chrome, the report published in Business Standard said.

A reason for UC Browsers success and widespread use was that it worked fast, and the browser made it easy to access content.

Marketwatch.com, citing the Wall Street Journal, said: Users say UC Browser works better in countries dominated by low-end smartphones and spotty mobile service.

On 29 June, when India banned 59 Chinese apps citing threats to national security and user privacy concerns, UC Browser was the most popular mobile browser after Google Chrome in India.

Data from Statcounter showed UC Browsers market share in India was 14.46 per cent while Chrome was at 75.56 percent market share between June 2019 and June 2020. Notable here is that even before the ban, Indian users were pulling away from UC Browser. A 2016 report from Medianama states that UC Browser at the time has a 58.4 per cent market share, the highest in India, with Opera coming in second at 16.5 per cent.

Also read: Google temporarily blocks access to banned Chinese apps in India

These numbers indicate how Google Chrome has managed to rapidly grow in India. The same 2016 Medianama report said Chrome had the biggest worldwide market share with 34.2 per cent.

Yet, in India, Chrome found no mention among the top few mobile browsers. Fast forward four years, the company has managed to corner three-fourths of Indian market for mobile browsers.

Now that UC Browser has been banned, low budget smart phone users can choose from multiple alternatives. There are many mobile browsers alternatives available, including Opera, Firefox, Chrome, Dolphin, Jio Browsers, etc, said Naiya.

Among the Indian companies, the replacements for UC Browser include Bharat Browser, Epic Privacy Browser and Indian Browser.

Also read:China says strongly concerned, verifying situation after India bans 59 Chinese apps

Prior to the June ban, which also cut out apps such as TikTok, SHAREit and Shein from the Indian market, UC Browser faced allegations for cyber attacks and fake news.

In 2018, DNA reported UC Bowser was among 42 apps flagged by intelligence agencies as potentials gateways for cyber attacks, spying and spreading fake news.

The year before, in 2017, it was removed from the Google Play Store. A company statement reported, a certain setting of UC Browser is not in line with Googles policy, according to a Financial Express report. It linked the apps removal to misleading campaigns it was running, as suggested by some media reports.

Two years prior to that, The Citizen Lab, a research lab based at University of Toronto, in 2015 found that UC Browser was leaking a significant amount of personal and personally-identifiable data of users. The research into UC Browser had been triggered by documents leaked by whistleblower Edward Snowden.

Also read: Shut up and put up What Chinese companies in India should do as Galwan crisis continues

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How Machine Learning Will Impact the Future of Software Development and Testing – ReadWrite

Machine learning (ML) and artificial intelligence (AI) are frequently imagined to be the gateways to a futuristic world in which robots interact with us like people and computers can become smarter than humans in every way. But of course, machine learning is already being employed in millions of applications around the worldand its already starting to shape how we live and work, often in ways that go unseen. And while these technologies have been likened to destructive bots or blamed for artificial panic-induction, they are helping in vast ways from software to biotech.

Some of the sexier applications of machine learning are in emerging technologies like self-driving cars; thanks to ML, automated driving software can not only self-improve through millions of simulations, it can also adapt on the fly if faced with new circumstances while driving. But ML is possibly even more important in fields like software testing, which are universally employed and used for millions of other technologies.

So how exactly does machine learning affect the world of software development and testing, and what does the future of these interactions look like?

A Briefer on Machine Learning and Artificial Intelligence

First, lets explain the difference between ML and AI, since these technologies are related, but often confused with each other. Machine learning refers to a system of algorithms that are designed to help a computer improve automatically through the course of experience. In other words, through machine learning, a function (like facial recognition, or driving, or speech-to-text) can get better and better through ongoing testing and refinement; to the outside observer, the system looks like its learning.

AI is considered an intelligence demonstrated by a machine, and it often uses ML as its foundation. Its possible to have a ML system without demonstrating AI, but its hard to have AI without ML.

The Importance of Software Testing

Now, lets take a look at software testinga crucial element of the software development process, and arguably, the most important. Software testing is designed to make sure the product is functioning as intended, and in most cases, its a process that plays out many times over the course of development, before the product is actually finished.

Through software testing, you can proactively identify bugs and other flaws before they become a real problem, and correct them. You can also evaluate a products capacity, using tests to evaluate its speed and performance under a variety of different situations. Ultimately, this results in a better, more reliable productand lower maintenance costs over the products lifetime.

Attempting to deliver a software product without complete testing would be akin to building a large structure devoid of a true foundation. In fact, it is estimated that the cost of post software delivery can 4-5x the overall cost of the project itself when proper testing has not been fully implemented. When it comes to software development, failing to test is failing to plan.

How Machine Learning Is Reshaping Software Testing

Here, we can combine the two. How is machine learning reshaping the world of software development and testing for the better?

The simple answer is that ML is already being used by software testers to automate and improve the testing process. Its typically used in combination with the agile methodology, which puts an emphasis on continuous delivery and incremental, iterative developmentrather than building an entire product all at once. Its one of the reasons, I have argued that the future of agile and scrum methodologies involve a great deal of machine learning and artificial intelligence.

Machine learning can improve software testing in many ways:

While cognitive computing holds the promise of further automating a mundane, but hugely important process, difficulties remain. We are nowhere near the level of process automation acuity required for full-blown automation. Even in todays best software testing environments, machine learning aids in batch processing bundled code-sets, allowing for testing and resolving issues with large data without the need to decouple, except in instances when errors occur. And, even when errors do occur, the structured ML will alert the user who can mark the issue for future machine or human amendments and continue its automated testing processes.

Already, ML-based software testing is improving consistency, reducing errors, saving time, and all the while, lowering costs. As it becomes more advanced, its going to reshape the field of software testing in new and even more innovative ways. But, the critical piece there is going to. While we are not yet there, we expect the next decade will continue to improve how software developers iterate toward a finished process in record time. Its only one reason the future of software development will not be nearly as custom as it once was.

Nate Nead is the CEO of SEO.co/; a full-service SEO company and DEV.co/; a custom web and software development business. For over a decade Nate had provided strategic guidance on technology and marketing solutions for some of the most well-known online brands. He and his team advise Fortune 500 and SMB clients on software, development and online marketing. Nate and his team are based in Seattle, Washington and West Palm Beach, Florida.

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How Machine Learning Will Impact the Future of Software Development and Testing - ReadWrite

This Machine Learning-Focused VC Firm Just Added A Third Woman Investment Partner – Forbes

Basis Set Ventures investment partners Chang Xu, Lan Xuezhao and Sheila Vashee are looking to run a ... [+] different kind of venture capital firm.

Basis Set Ventures doesnt want to be your typical venture capital firm. First, theres the fledgling VC firms focus on a technical area that has seen some disillusionment in recent years: machine learning and artificial intelligence. Sure, AI has become something out of startup bingo, tacked on in pitches and often stretched behind meaning. Basis Set founder Lan Xuezhao is confident she and her team can figure out whats real and whats not. We want to transform the way people work, she says.

Basis Set is different in another meaningful way, too: a woman-led VC firm, it has recently operated with two women investment partners in Xuezhao and Chang Xu, a partner who joined the firm from Upfront Ventures last year. Now, Basis Set has added its third woman investment partner in Sheila Vashee, giving the firm three women at the top of its investment committee.

Vashee joins Basis Set from Opendoor, where she led the unicorns growth team, including marketing, partnerships, operations and some of its product. Before her two-and-a-half year stint at Opendoor, Vashee was an early employee at Dropbox, where she helped oversee marketing and the launches of its business product. At Dropbox she sat close to Xuezhao, who joined in 2013 and led corporate development before departing to found Basis Set in 2017.

In an interview, Vashee says she decided to join Basis Set in part because of its thesis; in part because of a culture that operates differently from the typical venture shop. I believe that theres going to be a new wave of work tools that really revolutionizes every industry on every level, and I want to build that future, Vashee says.

Given its self-imposed focus on companies utilizing machine learning and AI, Basis Set has to be selective in what companies it pursues. Like other investors that use data to attempt to find better deals, Basis Sets data science team studies companies leadership and launches to move fast when attractive fundraises are coming together, hoping that its speed and accessibility will allow it to join rounds pursued by the best-known VC firms. A network of technical advisors, meanwhile, is intended to evaluate what startups are really using machine learning and AI in their core software.

We want to be superhuman in the sense that our data science team builds the armor that makes us see better, see further, run faster and process a lot more deals and high-quality investments, says Xuezhao.

With 50,000 founders in its database, the partners at Basis Set hope they can evaluate more startups in more places, including those who might fall into the blind spots of traditional VC because founders dont have the typical background or base their company in Silicon Valley. That includes the partners training the firms algorithms hands-on. Every morning, if Im not doing anything, Im in my inbox, saying whether a company is good for us and label data myself. That makes the system better, Xuezhao says.

So far, Basis Sets approach has led to investments such as Workstream, a hiring platform; Rasa, which provides conversational AI tools to big businesses; and Ike, which offers automation tools to the trucking industry. (It also includes Lime, a business that may use data but is known better for its rental scooters.) Vashee brings perspective from collaboration software and real estate software given her background, but the partners say their focuses within Basis Set are flexible.

Basis Sets partners hope that they stand out because of their young firms culture, too. Vashee says that her experience and Xuezhaos as professional moms with kids at home during the current Covid-19 work-from-home environment helps them relate to some founders who might not connect as well to more traditional VCs, but who are going through many of the same things: toddlers getting sick, the need to take early evening breaks and then get business done late into the night after the familys asleep.

My kids are right outside the door screaming now, and that wouldnt work in a normal VC, Vashee says. But I think integrating every part of our lives makes us better at everything we do, and actually makes founders relate to us better.

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This Machine Learning-Focused VC Firm Just Added A Third Woman Investment Partner - Forbes

The State-Of Machine Learning Adoption in the Enterprise – CIO Applications

Machine learning libraries with well-defined interfaces and documentation are becoming more accessible and therefore facilitating its adoption

We rely on Cross Functional Team setup (Develop Advocates), focus on Transformational or Disruptive solutions, Customer Pain points/Solutions and Communication/Marketing.

How do you see the evolution of machine learning within the next few years with regard to some of its potential disruptions and transformations?

Industry is still navigating throughout the ML hype. There are a few ML-based applications that have been successfully deployed and added to applications found in the marketplace. For example, voice and image recognition, service brokering and matchmaking, consumer forecast, etc. have found their place in the domestic use but these are still far away from truly becoming disruptions in the industrial space.

Critical aspects in the success of ML evolution are:

The reduction in complexity for mapping the domain expertise to ML-based solutions. Today there is no straightforward path to transfer domain knowledge to the data scientists where there is still a high dependency on.

As we continue to mature and descend from the ML hype, we will soon realize that not all industrial processes are suited for ML. This aspect still needs to be settled.

Provide greater access to ML automation.

Legacy systems (server/IaaS based) are decelerators in the ML evolution. These tools need to undergo structural upgrades to be able to cope with the new wave of data and analytics requirements (scalability, volume, speed, multitenancy, etc.). New data and compute frameworks are going to be needed to reduce complexity while increasing automation.

Agile change management cycles.

ML-model management soon to become a critical-path need.

A workforce skillset aligned with the know-how to map ML to domain expertise is precious.

What would be the single piece of advice that you could impart to a fellow or aspiring professional in your field embarking on a similar venture or professional journey along the lines of your service and area of expertise?

Think outside the box. Protect a portion of your resources allocated to transformation.

Use Open Source technologies and university partnership and internship programs to pilot solutions to prove out ROI. Companies have been restricting development to their internally conceived software solutions. However, it is now understood that no single player will be able to provide all the pieces of the overall solution. Therefore, there is value in looking for potential partnerships that would increase the chances to success.

Make IP solutions accessible to the industry and let other ideas into the internal design process. This implies the need for a cultural transformation. Look at effective business and pricing models. Perhaps one can achieve more effective business by partnering accordingly. And lastly, using resources to create an all-encompassing solution hinders the ability of a company to rapidly adapt to a fast-pace technology evolution. So develop while youre small and then grow.

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The State-Of Machine Learning Adoption in the Enterprise - CIO Applications

Machine Learning: The Future from the Perspective of Model Building – CIO Applications

A good example of this is using specific gestures to raise or lower the volume or to changetracks--instead of pushing buttons to navigate your cars entertainment system. Some companies, such as Arcturus Networks, are building software modules for surveillance cameras and then selling them to camera manufacturers for integration into their end products. These are just a few examples of the types of companies that are popping up with specialties related to application functions.

Everybody can talk about a neural network, but it is essential to understand what it really means and the value it brings to finding other ways of solving problems

The main driver for us is figuring out how to make open-source technologies easier for our customers to use. The NXP eIQ Machine Learning Software Development Environment is continuously expanding to include model conversion for a wide range of NN frameworks and inference engines, such as TensorFlow Lite and Glow (the PyTorch Compiler). There are also open-source technologies from Arm, such as Arm NN, that will enable higher performance machine learning on ArmCortex Aprocessors. We are even using open-source inference engines to enable machine learning accelerators in our devices. Case in point is our new device called the i.MX 8M Plus. This is our first applications processor featuring an integrated machine learning accelerator that delivers two to three times more performance than NXP devices without it. And, integrating higher performance machine learning capability with acceleration is one of the emerging trends in the industry.

Whats Next?

The problem is that machine learning, or AI in general, is such a fast-growing area. The good and bad is that there have been far too many different technologies to keep up with and for us to support. Moving into the future, the technology around today will either be merged or well start to see more de facto standards. For instance, TensorFlow is something thats not going to go away and represents a significant share of the machine learning developers. On the other hand, PyTorch has quickly been gaining in popularity, especially in the academic community. Other similar technologies created with a specific purpose in mind may be useful, but industry adoption is low. These outliers may merge or disappear in the future. This is perhaps one of the main trends that I see moving forward.

A few years down the road, machine learning will become a de facto standard, and youll see it implemented in a majority of devices because people will realize that its not magicand the good tools that are already available to make it work are getting better. And, you dont have to be a data scientist or an expert in understanding neural network technology to integrate machine learning into your platform. And thats one area where we also spend a lot of time at NXP -- how do we make it easier for customers to deploy their machine learning models on our devices. We see both performance improvements and memory size reductions as the technology is becoming more optimized, so thats going to be a significant way forward.

Piece of Advice

As previously mentioned, we have developed a technology called eIQ for edge intelligence. I encourage people to check it out, try walking through some of the application examples, and experience machine learning in action. Like most of us, if youre trying to learn more about this technology, there are many good YouTube videos and an abundance of articles you just have to spend the time filtering through them. But you can learn a lot by what people have posted online: everything from the basics of what is a neural network, how to train a neural network, how to make it more performance efficient and more accurate, and so on. Theres plenty of information available for people who are starting. One exciting thing about machine learning, which applies to other technologies as well, is that the more you learn about it, the more you realize you dont know. Everybody can talk about a neural network, but understanding what it really means and its value in solving problems is essential to unlocking machine learnings extraordinary potential.

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Machine Learning: The Future from the Perspective of Model Building - CIO Applications

Maintaining the Human Element in Machine Learning Gigaom – Gigaom

Thought Leadership Webinars

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Join us for this free 1-hour webinar from GigaOm Research. The webinar features GigaOm analyst Andrew Brust and special guest Nicolas Omont from Dataiku, a leader across the entire AI lifecycle.

In this 1-hour webinar, you will discover:

Machine learning (ML) and ML operations platforms are becoming increasingly popular and sophisticated. Thats a good thing, as it transforms AI initiatives from science projects to rigorous engineering efforts. But with such platforms comes the temptation of automation, scripting the whole ML process, not just optimizing models, but monitoring their drift in accuracy and retraining them. While some automation is good, humans play a critical role.

Elements of fairness are contextual and involve tradeoffs. Changes in data may require retraining or restructuring a models features, depending on circumstances and current events. All of this requires human judgment, carefully integrated with automated management and algorithmic learning. Humans have to be part of the workflow, included in the feedback loop, and involved in the process.

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Maintaining the Human Element in Machine Learning Gigaom - Gigaom

How Will Machine Learning Serve the Hotel Industry in 2020 and Beyond? – CIO Applications

Fremont, CA:Artificial intelligence (AI) implementation grew tremendously last year alone such that any business that does not consider the implications of machine learning (ML) will find itself in multiple binds. It has become mandatory that companies should question themselves how they will utilize machine learning to reap its benefits while staying in business. Similarly, hotels should interrogate themselves about how they will use ML. However, trying to catch-up with this technology is potentially dangerous when companies realize that their competition is outperforming them. When hotels believe that robotic housekeepers and facial recognition kiosks are the effective applications of ML, they can do much more. Here is how ML serves the hotel industry while helping save money, improve service, and grow more efficient.

For successfully running the hotel industry, energy and water are the two most important factors. Will there be a no if there is a technology that controls the use of the two critical factors without affecting the guests comfort zone. Every dollar saved on energy and water can impact the bottom line of the business in a big way. Hotels can track the actual consumption of energy against predictive models allowing them to manage performance against competitors. Hotel brands can link-in room energy to the PMS so that when the room is empty, the heater or any other electrical appliances, automatically turns off.

ML helps brands hire suitable candidates and also highly qualified candidates who might have been overlooked for not fulfilling traditional expectations. ML algorithms were used to create assessments to test candidates for recruiting against the personas using gamification-based tools. Further, ML maximizes the value of premium inventory and increases guest satisfaction by offering guests personalized upgrades based on their previous stay at a price that the guest is ready to pay at booking and pre-arrival period. Using ML technology, hotel brands can create offers at any point during the guest stay, including the front desk. Thus, the future of sustainability in the hospitality industry relies on ML.

See also:Top Food Service Management Solution Companies

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How Will Machine Learning Serve the Hotel Industry in 2020 and Beyond? - CIO Applications

Overcoming the Explainability Challenges of Machine Learning Models – Machine Learning Times – machine learning & data science news – The…

Some History Machine Learning Models, which have historically been referred to as predictive models, are not new. Any early practitioner in this field would emphasize that the two key deliverables of any model are as follows: its benefits to the business or organization Model Explainability (i.e. what is inside the model) The model benefits are essentially about optimizing ROI where the challenge might be to identify those key metrics that impact ROI. For a marketing campaign, the use of the model helps the marketer to better allocate his or her budget towards those individuals who are more likely to respond

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Overcoming the Explainability Challenges of Machine Learning Models - Machine Learning Times - machine learning & data science news - The...

Advanced analytics and machine learning the connected airport takes flight – Passenger Terminal Today

As restrictions on air travel begin to lift and the volume of air travelers starts to increase, airport operators will be leveraging an extensive range of innovative technologies in a bid to streamline passenger journeys, deliver more personalized experiences, and optimize operational capacity and efficiencies.

The key is data. By generating, analyzing and acting on a variety of new datapoints, airport operators are able to provide a host of innovative processes and services to improve operations. From mobile apps that help passengers navigate the facilities and services on offer prior to embarkation and stay updated on their travel arrangements, to self-service check-in and artificial intelligence (AI) powered customer service chatbots, the airport of today is evolving at breakneck speed.

On top of this, automation technologies help airport operators reduce queues and streamline passenger movements through key security controls, immigration and gate checkpoints. Data-powered services are also offering passengers a more frictionless journey experience.

The connected airport takes flight

The emergence of Internet of Things (IoT) smart sensors and facial recognition technologies means that todays airport operators have access to huge volumes of data. Whats more, they are primed and ready to use a variety of cutting-edge solutions that will make it easier to leverage insight from this data to optimize their operational capabilities. These technologies promise to make airports and the surrounding infrastructure safer and more efficient than ever before.

Using AI algorithms and digital twin technologies, operators will soon be able to collate data from across their real-time airport and airline operations to visualize, simulate and predict with greater certainty exactly what is likely to happen next. Leveraging these insights, theyll be able to trigger proactive responses to any anticipated event.

Sharing this operational data with other stakeholders, including airline operators, theyll be able to monitor passenger numbers and identify their key characteristics, all of which will make it easier to turn around facilities faster and ensure that appropriate human and equipment resources are in the right place, at the right time.

Meanwhile, a growing number of connected and autonomous vehicles and robots are already making an appearance in airports around the globe.

Baggage and luggage: using analytics for just-in-time operations

One key area where transformational technologies are making an impact now for airports, airlines and ground handlers is by better tracking the billions of bags that are transported every year. This technology is already rolling out across the world and is set to make it easier for passengers to track their bags progress, from the moment they deposit it to the final delivery into their hands once they reach their end destination.

Following the 2018 introduction of IATAs Resolution 753, which requires baggage to be tracked at key points passenger handover to airline, loading to the aircraft, delivery to transfer area and return to the passenger airports have been turning to data collection and analytics to enhance the entire extended chain of custody.

Alongside addressing the challenge of baggage mishandling, increasing the efficiency of their baggage operations and delivering an enhanced passenger experience, the introduction of these technologies has also enabled airports to work more closely with airlines to keep airplanes and passengers safer. Key to this is clamping down on the illicit activities of airside and landside personnel.

For example, analytics can spot unusual patterns such as bags unexpectedly entering the system on loading, or baggage handlers who are associated with baggage that is persistently misrouted. Consider items such as an extra bag surreptitiously checked into the baggage system by a bad-actor baggage handleraftera passenger has boarded. The extra bag might contain goods for resale such as rare apparel, or items subject to high tariffs. When claimed by an accomplice at the destination, the passenger would never know about the illegal use of their identity nor would the airline know of its criminal exploitation. Analytics technologies identify this type of misuse by analyzing the anomalous patterns from the activity logs of the handlers and the bags themselves.

Tightening security controls

The adoption of machine learning and the integration of AI with airport security systems such as screening, perimeter security and surveillance is enabling airport authorities to initiate additional security layers designed to protect the safety of employees and passengers alike. This includes implementing enhanced risk-based screening measures, behavioral recognition and modeling systems and state-of-the art 3D checkpoint scanners, as well as using smart gates and enhanced facial recognition at every stage of a departing passengers journey.

Systems that can detect and pinpoint risky behaviors can be used to detect disgruntled passengers or airport/airline staff, all of which helps improve the detection of potential security threats.

Protecting critical assets

Clearly, as automated airside operations become a reality, airport management teams will increasingly be dependent on leveraging real-time data to reduce the variability of operational processes and improve performance.

Integrating operational silos to facilitate the real-time information flows that feed their complex adaptive systems logistics, customer-facing services and airline operators is just the start. Keeping such highly connected environments protected from external cyber threats that attempt to access assets will increasingly become a top priority if airport operators are to realize the benefits of their technology investments.

With airports considered critical infrastructure, a growing awareness of data and its inherent risks is a must-have.

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Advanced analytics and machine learning the connected airport takes flight - Passenger Terminal Today