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Theres a pervasive sense of legislative paralysis gripping Capitol Hill. And its been there long before the impeachment inquiry began.

For months, President Donald Trump has fired off tweet missives accusing House Democrats of getting nothing done in Congress, and being consumed with impeachment.

Trump may want to look to the Republican-controlled Senate instead. Democrats in the House have been passing bills at a rapid clip; as of November 15, the House has passed nearly 400 bills, not including resolutions. But the House Democratic Policy and Communications Committee estimates 80 percent of those bill have hit a snag in the Senate, where Majority Leader Mitch McConnell is prioritizing confirming judges over passing bills.

Congress has passed just 70 bills into law this year. Granted, it still has one more year in its term, but the number pales in comparison to recent past sessions of Congress, which typically see 300-500 bills passed in two years (and that is even a diminished number from the 700-800 bills passed in the 1970s and 1980s).

Ten of those 70 bills this year have been renaming federal post offices or Veterans Affairs facilities, and many others are related to appropriations or extending programs like the National Flood Insurance Program or the 9/11 victim compensation fund.

This has led to House Democrats decrying McConnells so-called legislative graveyard, a moniker the Senate majority leader has proudly adopted. McConnell calls himself the grim reaper of Democratic legislation he derides as socialist, but many of the bills that never see the Senate floor are bipartisan issues, like a universal background check bill, net neutrality, and reauthorizing the Violence Against Women Act.

From raising the minimum wage to ensuring equal pay, we have passed legislation to raise wages. And we have passed legislation to protect and expand health coverage and bring down prescription drug prices, House Majority Leader Steny Hoyer said in a statement to Vox. We continue to urge Senator McConnell to take up our bills, many of which are bipartisan.

McConnell is focused on transforming the federal judiciary instead, with the Senate confirming over 150 of Trumps nominees to the federal bench. And he has refused to bring Democratic bills to the Senate floor in part to protect vulnerable Republican senators from having to take tough votes that could divide the GOP ahead of the 2020 election. Still, some Senate Republicans fear inaction could make them just as vulnerable.

Im very eager to turn from nominations to legislation, Sen. Susan Collins (R-ME) recently told the New York Timess Carl Hulse. There are important issues that are pending, and I think we could produce some terrific bills that would be signed into law.

Lately, Republicans and Trump are accusing Democrats of single-mindedly pursuing impeachment at the detriment of passing bills.

Again, the more accurate picture is that Democrats have been passing a lot of bills in addition to investigating the president. But split control of government and Trumps fury at being investigated by Democratic committees paralyzed Washingtons legislative functions well before impeachment proceedings began in the fall.

Back in May, Trump was blasting Democrats for not making enough progress on infrastructure, health care, and veterans issues. His complaints intensified after an explosive White House meeting on infrastructure between Trump and Democrats the day before, which the president walked out of.

Their heart is not into Infrastructure, lower drug prices, pre-existing conditions and our great Vets, Trump tweeted. All they are geared up to do, six committees, is squander time, day after day, trying to find anything which will be bad for me.

Months later, the presidents complaints remain the same. He recently tweeted, Nancy Pelosi, Adam Schiff, AOC and the rest of the Democrats are not getting important legislation done, hence, the Do Nothing Democrats.

Trump isnt the only one with a perception that very little is happening in Congress. Congresss approval rating is a dismal 24 percent, with 72 percent disapproval, according to Gallup.

During the Republican-controlled Congress in 2017 and 2018, the two major legislative accomplishments of McConnell, Trump, and House Speaker Paul Ryan were a massive GOP tax cut and a bipartisan criminal justice reform bill in 2018. The very end of Ryans time as speaker also saw Trump drive a government shutdown that continued into Pelosis tenure in 2019.

Since Democrats took control of the House, the few things theyve been able to agree with Senate Republicans on include a bill to reopen the federal government after a three-week shutdown, a resolution to end US involvement in the war in Yemen (which was vetoed by Trump), and a disaster aid agreement. But other big-ticket items Democrats hoped to achieve, like an infrastructure package and a prescription drug bill, have yet to be passed.

As we near the end of the year, much of the media focus will continue to be on impeachment. House Democrats will also be focused on a vote on a major bill to lower prescription drug costs (something Trump has said is a priority for him), the Voting Rights Advancement Act, and the National Defense Authorization Act.

Just because impeachment is the main story in Washington doesnt mean policy work isnt happening. It just means it isnt getting talked about as much, and that the president a figure who could apply pressure on McConnell to take up some of the bipartisan legislation currently gathering dust has other priorities.

Given the Senate could soon be consumed by an impeachment trial, the remaining weeks of 2019 could be the final opportunity for lawmakers in the upper chamber to advance legislation. However, there are no signals that Republican Senate leaders will seize that opportunity.

House Democrats have passed a wide range of bills since they came to power in January, ranging from a sweeping anti-corruption and pro-democracy reform known as H.R.1, to bills to save net neutrality, pass universal background checks for guns, and reenter the United States into the Paris climate accords.

They have also put a large emphasis on health care, a defining issue of the 2018 election after Trump and Senate Republicans attempted to pass a bill to repeal and replace the Affordable Care Act. Democrats have focused on bills to lower prescription drug costs, protect preexisting conditions, and condemning the Trump administrations legal battle to strike down the ACA in the courts. And although Medicare-for-all is driving the conversation in the 2020 presidential primary, it has not gotten a vote in the House.

Much of this agenda is sitting in the Senate. There have been a few things House Democrats and Senate Republicans have agreed on: disaster relief aid, reopening the government after the shutdown, the resolution to end US involvement in the Yemen war, a bill to protect public lands, and a resolution disapproving of Trumps use of emergency powers.

But on major policy issues like health care and infrastructure, or even bipartisan ones like net neutrality, the Equal Pay Act, or even a simple reauthorization of the longstanding Violence Against Women Act Democrats bills are continuing to languish in the Senate. House Democrats are expecting to take up House Resolution 3, a major health care bill to lower the cost of prescription drugs, before the Christmas break. Although were not going to list all 400 bills for brevitys sake, heres a list of major bills and resolutions the House has passed so far.

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House Democrats have passed hundreds of bills. Trump and Republicans are ignoring them - Vox.com

This evening should offer a clue as to where Angel Gomes sits in the Manchester United pecking order.

Gomes impressed in Uniteds Europa League game against Astana, but did he do enough to work his way into the first team squad?

Tonight provides United with a very distinct choice over Gomes, with two fixtures to be played.

The first game is the match we have all been concentrating on. Manchester United v Tottenham Hotspur in the Premier League.

The other one is Tranmere Rovers v Manchester Uniteds under-21s in the EFL Trophy.

We are not expecting or even calling for Gomes to be named as a starter against Tottenham.

It would be a positive step if he is named on the bench against Spurs.

Gomes wont fear Tottenham at all. He showed his quality against them in pre-season in China when he danced through their defence before scoring a winner from a tight angle.

It was a sign of what he is capable of and why United need to be patient with him.

This is a chance for United to show faith in him and bring him into the squad, and encourage him to do the same again.

What is the benefit of playing or naming Juan Mata on the bench? He was terrible against Villa at the weekend and must soon be running out of chances.

Gomes is probably most likely to be with the under-21s. This is a knockout game against Tranmere and United will want to put a good team out and get to the next round.

The England under-20 international played in one of the three group stage games, with United winning all of them while conceding just once.

Gomes could get another chance in the Europa League against AZ Alkmaar, or Colchester in the Carabao Cup later this month.

This could be a good chance for him to get more game time, and United may be interested to see the attitudes of their young players who featured against Astana and see how they respond.

It would be a shame for Gomes to miss out on the first team game against Spurs after what he did against them in July. That might not be lost on him.

United are trying to get him to sign a new contract, and this distinct choice which could see him back with the under-21s may be frustrating for him.

If that decision is made, United need to sell it to him that he needs the minutes, and if he does well he will be back with the first team for the cup games.

United are low on options for the playmaker position at first team level, and Gomes has potential.

Jose Mourinho gave him his debut back in 2017. It would be a great story if he came on to make an impact against him tonight. But he will probably be at Tranmeres Prentice Park instead.

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Angel Gomes has an important night for United, but for which team? - United In Focus - Manchester United FC News

The holiday season is here and there are rafts of new associates manning registers and helping stores handle swarms of shoppers. But how are these associates finding their seasonal roles? Many retailers already use Artificial Intelligence (AI) in their recruiting systems for hiring the best and brightest seasonal help. Human resource tasks such as screening and hiring are more efficient and accurate thanks to the AI envisioned a few years ago and in operation today.

Nevertheless, landing a good employee is only the first step in making this season a winning year for shopper loyalty, conversions, and same-store sales. Once hired, every new employee instantly becomes a brand ambassador and critical resource for shoppers exploring the store, possibly for the first time. That good hire must quickly become a great ambassador or the bad news will travel fast. According to Andrew Thomas, founder of Skybell Video Doorbell, it takes roughly 40 positive customer experiences to undo the damage of a single negative review.

Read More: The Future of Works Most Crucial Component: Artificial Intelligence

Great ambassadors need help getting started and AI is about to tackle the problems of spinning up new employees.

New and existing employees serve the customer best when they are energetic, motivated and supported. Training is seldom effective in bringing out these essential human characteristics because they are an in-the-moment-every-moment responsibility. These human behaviors do not fit easily in a classroom when the real challenge occurs on the retail floor. Whats needed for behavioral support is continuous attitudinal awareness, gentle encouragement, motivational nudging and a supportive buddy who is always available.

Employees represent the company best when they know the products, have clarity of the brand and timely exposure to proven Sales messages a daunting challenge for a new employee in an industry where training time is costly and the Sales floor is frequently chaotic. Retailers need to know exactly which information needs reinforcing, who needs to hear the information, how much information they can digest when the best time to deliver it is and in which location the information makes the most educational impact.

Read More: Top 5 Best Pay Per Click Marketing Services in Dubai

The obvious vehicle for delivering both support and education to new employees is clear and continuous communication with veteran employees. Unfortunately, our 1950s walkie-talkies and our high-tech heads-down smart devices do not solve the problem nor fit the retail floor. The former clutters the ear with mostly irrelevant chatter, while the latter destroys both situational awareness and shopper rapport.

Whats needed is a conversational platform powered by Natural Language Processing that connects employees with each other or with the information available in the company IT systems on the spot, without having to rely on a screen. Using intelligent mediation within the communication platform assures each employee gets the best information, at the right time, in the right location.

Once a conversational platform replaces the old walkie-talkies, regular mobile devices become occasional-use, specialized tools. The AI platform learns the environment and transforms the employees measurably improving associate effectiveness and the shopper experience. Todays conversational platforms are able to add AI that dissects conversations and directs information to specific employees, at specific times, in specific locations. This information may be anything from the name of the customer approaching them to collect an online order in the store, a register backup call (with the opportunity to instantly respond), or an accurate technical answer to a question from an expert group anywhere in the world.

The conversations enabled between employees across the store contain the full context of what they know and offers management insight into how they share when they inspire, and where they perform the best. The conversations contain solutions.

AI is a natural progression in the evolution of conversational platforms for mobile store team members. The platforms available today connect employees, groups and IT systems using intelligent mediation while simultaneously collecting data for measuring performance. It wont be long before AI overlays these platforms with deep analytics of employee behaviors, derivation of critical messaging, and quantum leaps in shopper experiences.

Read More: The Future of AI: More Automation and Less Empathic Interaction

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Artificial Intelligence (AI) and the Seasonal Associate - AiThority

Image credit: Depositphotos

This article is part ofDemystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI.

Last week, Lee Se-dol, the South Korean Go champion who lost in a historical matchup against DeepMinds artificial intelligence algorithm AlphaGo in 2016, declared his retirement from professional play.

With the debut of AI in Go games, Ive realized that Im not at the top even if I become the number one through frantic efforts, Lee told theYonhap news agency. Even if I become the number one, there is an entity that cannot be defeated.

Predictably, Se-dols comments quickly made the rounds across prominent tech publications, some of them using sensational headlines with AI dominance themes.

Since the dawn of AI, games have been one of the main benchmarks to evaluate the efficiency of algorithms. And thanks to advances in deep learning and reinforcement learning, AI researchers are creating programs that can master very complicated games and beat the most seasoned players across the world. Uninformed analysts have been picking up on these successes to suggest that AI is becoming smarter than humans.

But at the same time, contemporary AI fails miserably at some of the most basic that every human can perform.

This begs the question, does mastering a game prove anything? And if not, how can you measure the level of intelligence of an AI system?

Take the following example. In the picture below, youre presented with three problems and their solution. Theres also a fourth task that hasnt been solved. Can you guess the solution?

Youre probably going to think that its very easy. Youll also be able to solve different variations of the same problem with multiple walls, and multiple lines, and lines of different colors, just by seeing these three examples. But currently, theres no AI system, including the ones being developed at the most prestigious research labs, that can learn to solve such a problem with so few examples.

The above example is from The Measure of Intelligence, a paper by Franois Chollet, the creator of Keras deep learning library. Chollet published this paper a few weeks before Le-sedol declared his retirement. In it, he provided many important guidelines on understanding and measuring intelligence.

Ironically, Chollets paper did not receive a fraction of the attention it needs. Unfortunately, the media is more interested in covering exciting AI news that gets more clicks. The 62-page paper contains a lot of invaluable information and is a must-read for anyone who wants to understand the state of AI beyond the hype and sensation.

But I will do my best to summarize the key recommendations Chollet makes on measuring AI systems and comparing their performance to that of human intelligence.

The contemporary AI community still gravitates towards benchmarking intelligence by comparing the skill exhibited by AIs and humans at specific tasks, such as board games and video games, Chollet writes, adding that solely measuring skill at any given task falls short of measuring intelligence.

In fact, the obsession with optimizing AI algorithms for specific tasks has entrenched the community in narrow AI. As a result, work in AI has drifted away from the original vision of developing thinking machines that possess intelligence comparable to that of humans.

Although we are able to engineer systems that perform extremely well on specific tasks, they have still stark limitations, being brittle, data-hungry, unable to make sense of situations that deviate slightly from their training data or the assumptions of their creators, and unable to repurpose themselves to deal with novel tasks without significant involvement from human researchers, Chollet notes in the paper.

Chollets observations are in line with those made by other scientists on the limitations and challenges of deep learning systems. These limitations manifest themselves in many ways:

Heres an example: OpenAIs Dota-playing neural networks needed 45,000 years worth of gameplay to reach a professional level. The AI is also limited in the number of characters it can play, and the slightest change to the game rules will result in a sudden drop in its performance.

The same can be seen in other fields, such as self-driving cars. Despite millions of hours of road experience, the AI algorithms that power autonomous vehicles can make stupid mistakes, such as crashing into lane dividers or parked firetrucks.

One of the key challenges that the AI community has struggled with is defining intelligence. Scientists have debated for decades on providing a clear definition that allows us to evaluate AI systems and determine what is intelligent or not.

Chollet borrows the definition by DeepMind cofounder Shane Legg and AI scientist Marcus Hutter: Intelligence measures an agents ability to achieve goals in a wide range of environments.

Key here is achieve goals and wide range of environments. Most current AI systems are pretty good at the first part, which is to achieve very specific goals, but bad at doing so in a wide range of environments. For instance, an AI system that can detect and classify objects in images will not be able to perform some other related task, such as drawing images of objects.

Chollet then examines the two dominant approaches in creating intelligence systems: symbolic AI and machine learning.

Early generations of AI research focused on symbolic AI, which involves creating an explicit representation of knowledge and behavior in computer programs. This approach requires human engineers to meticulously write the rules that define the behavior of an AI agent.

It was then widely accepted within the AI community that the problem of intelligence would be solved if only we could encode human skills into formal rules and encode human knowledge into explicit databases, Chollet observes.

But rather than being intelligent by themselves, these symbolic AI systems manifest the intelligence of their creators in creating complicated programs that can solve specific tasks.

The second approach, machine learning systems, is based on providing the AI model with data from the problem space and letting it develop its own behavior. The most successful machine learning structure so far is artificial neural networks, which are complex mathematical functions that can create complex mappings between inputs and outputs.

For instance, instead of manually coding the rules for detecting cancer in x-ray slides, you feed a neural network with many slides annotated with their outcomes, a process called training. The AI examines the data and develops a mathematical model that represents the common traits of cancer patterns. It can then process new slides and outputs how likely it is that the patients have cancer.

Advances in neural networks and deep learning have enabled AI scientists to tackle many tasks that were previously very difficult or impossible with classic AI, such as natural language processing, computer vision and speech recognition.

Neural networkbased models, also known as connectionist AI, are named after their biological counterparts. They are based on the idea that the mind is a blank slate (tabula rasa) that turns experience (data) into behavior. Therefore, the general trend in deep learning has become to solve problems by creating bigger neural networks and providing them with more training data to improve their accuracy.

Chollet rejects both approaches because none of them has been able to create generalized AI that is flexible and fluid like the human mind.

We see the world through the lens of the tools we are most familiar with. Today, it is increasingly apparent that both of these views of the nature of human intelligenceeither a collection of special-purpose programs or a general-purpose Tabula Rasaare likely incorrect, he writes.

Truly intelligent systems should be able to develop higher-level skills that can span across many tasks. For instance, an AI program that masters Quake 3 should be able to play other first-person shooter games at a decent level. Unfortunately, the best that current AI systems achieve is local generalization, a limited maneuver room within their own narrow domain.

In his paper, Chollet argues that the generalization or generalization power for any AI system is its ability to handle situations (or tasks) that differ from previously encountered situations.

Interestingly, this is a missing component of both symbolic and connectionist AI. The former requires engineers to explicitly define its behavioral boundary and the latter requires examples that outline its problem-solving domain.

Chollet also goes further and speaks of developer-aware generalization, which is the ability of an AI system to handle situations that neither the system nor the developer of the system have encountered before.

This is the kind of flexibility you would expect from a robo-butler that could perform various chores inside a home without having explicit instructions or training data on them. An example is Steve Wozniaks famous coffee test, in which a robot would enter a random house and make coffee without knowing in advance the layout of the home or the appliances it contains.

Elsewhere in the paper, Chollet makes it clear that AI systems that cheat their way toward their goal by leveraging priors (rules) and experience (data) are not intelligent. For instance, consider Stockfish, the best rule-base chess-playing program. Stockfish, an open-source project, is the result of contributions from thousands of developers who have created and fine-tuned tens of thousands of rules. A neural networkbased example is AlphaZero, the multi-purpose AI that has conquered several board games by playing them millions of times against itself.

Both systems have been optimized to perform a specific task by making use of resources that are beyond the capacity of the human mind. The brightest human cant memorize tens of thousands of chess rules. Likewise, no human can play millions of chess games in a lifetime.

Solving any given task with beyond-human level performance by leveraging either unlimited priors or unlimited data does not bring us any closer to broad AI or general AI, whether the task is chess, football, or any e-sport, Chollet notes.

This is why its totally wrong to compare Deep Blue, Alpha Zero, AlphaStar or any other game-playing AI with human intelligence.

Likewise, other AI models, such as Aristo, the program that can pass an eighth-grade science test, does not possess the same knowledge as a middle school student. It owes its supposed scientific abilities to the huge corpora of knowledge it was trained on, not its understanding of the world of science.

(Note: Some AI researchers, such as computer scientist Rich Sutton, believe that the true direction for artificial intelligence research should be methods that can scale with the availability of data and compute resources.)

In the paper, Chollet presents the Abstraction Reasoning Corpus (ARC), a dataset intended to evaluate the efficiency of AI systems and compare their performance with that of human intelligence. ARC is a set of problem-solving tasks that tailored for both AI and humans.

One of the key ideas behind ARC is to level the playing ground between humans and AI. It is designed so that humans cant take advantage of their vast background knowledge of the world to outmaneuver the AI. For instance, it doesnt involve language-related problems, which AI systems have historically struggled with.

On the other hand, its also designed in a way that prevents the AI (and its developers) from cheating their way to success. The system does not provide access to vast amounts of training data. As in the example shown at the beginning of this article, each concept is presented with a handful of examples.

The AI developers must build a system that can handle various concepts such as object cohesion, object persistence, and object influence. The AI system must also learn to perform tasks such as scaling, drawing, connecting points, rotating and translating.

Also, the test dataset, the problems that are meant to evaluate the intelligence of the developed system, are designed in a way that prevents developers from solving the tasks in advance and hard-coding their solution in the program. Optimizing for evaluation sets is a popular cheating method in data science and machine learning competitions.

According to Chollet, ARC only assesses a general form of fluid intelligence, with a focus on reasoning and abstraction. This means that the test favors program synthesis, the subfield of AI that involves generating programs that satisfy high-level specifications. This approach is in contrast with current trends in AI, which are inclined toward creating programs that are optimized for a limited set of tasks (e.g., playing a single game).

In his experiments with ARC, Chollet has found that humans can fully solve ARC tests. But current AI systems struggle with the same tasks. To the best of our knowledge, ARC does not appear to be approachable by any existing machine learning technique (including Deep Learning), due to its focus on broad generalization and few-shot learning, Chollet notes.

While ARC is a work in progress, it can become a promising benchmark to test the level of progress toward human-level AI. We posit that the existence of a human-level ARC solver would represent the ability to program an AI from demonstrations alone (only requiring a handful of demonstrations to specify a complex task) to do a wide range of human-relatable tasks of a kind that would normally require human-level, human-like fluid intelligence, Chollet observes.

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Artificial intelligence: How to measure the I in AI - TechTalks

LONDON--(BUSINESS WIRE)--The artificial intelligence market in the US education sector is expected to post a CAGR of nearly 48% during the period 2018-2022, according to the latest market research report by Technavio. Request a free sample report

The increasing emphasis on customized learning paths using AI will be one of the major drivers in the global artificial intelligence market in the US education sector. The education system of the US is well developed and teachers and students in the country are aware about AI technology. This increases the adoption of artificial intelligence in the education sectors of the US. Moreover, the growing reliance on machine learning technologies for the collection of data about student performance will contribute to expanding the artificial intelligence market in the US education sector. Also, the availability of advanced AI-based content delivery software at affordable prices in the US will boost the market growth during the forecast period.

To learn more about the global trends impacting the future of market research, download free sample: https://www.technavio.com/talk-to-us?report=IRTNTR22412

As per Technavio, the increased emphasis on chatbots, will have a positive impact on the market and contribute to its growth significantly over the forecast period. This research report also analyzes other important trends and market drivers that will affect market growth over 2018-2022.

Artificial Intelligence Market in the US Education Sector: Increased Emphasis on Chatbots

The increased emphasis on chatbots will be one of the critical trends of the artificial intelligence market in the US education sector. Chatbots are increasingly being used by schools and colleges in the US. Chatbots use AI, ML, and deep learning technologies, to store, process, and communicate data to students. Moreover, chatbots have the capability of performing multiple functions, including conversations with students and answering queries. They can perform a diverse set of tasks and can also be used to evaluate, and correct assessments submitted by students. As the scope of chatbots is increasing, research on the applicability of chatbots is creating new opportunities for vendors, which will propel the market growth during the forecast period.

The rising focus on content analytics and the increasing crowdsourced tutoring are some other major factors that will boost market growth during the forecast period, says a senior analyst at Technavio.

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Artificial Intelligence Market in the US Education Sector: Segmentation Analysis

This market research report segments the artificial intelligence market in the US education sector by education model (learner model, pedagogical model, and domain model) and end-user (higher education sector and K-12 sector).

The learner model will witness the highest incremental growth during the forecast period of 2018-2022. However, the higher education sector will account for the largest market share due to student knowledge in the use of modern technology.

Technavios sample reports are free of charge and contain multiple sections of the report, such as the market size and forecast, drivers, challenges, trends, and more.

Request a free sample report

Some of the key topics covered in the report include:

Market Landscape

Market Sizing

Five Forces Analysis

Market Segmentation

Geographical Segmentation

Market Drivers

Market Challenges

Market Trends

Vendor Landscape

About Technavio

Technavio is a leading global technology research and advisory company. Their research and analysis focuses on emerging market trends and provides actionable insights to help businesses identify market opportunities and develop effective strategies to optimize their market positions.

With over 500 specialized analysts, Technavios report library consists of more than 17,000 reports and counting, covering 800 technologies, spanning across 50 countries. Their client base consists of enterprises of all sizes, including more than 100 Fortune 500 companies. This growing client base relies on Technavios comprehensive coverage, extensive research, and actionable market insights to identify opportunities in existing and potential markets and assess their competitive positions within changing market scenarios.

If you are interested in more information, please contact our media team at media@technavio.com.

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Artificial Intelligence Market in the US Education Sector 2018-2022 | Increased Emphasis on Chatbots to Boost Growth | Technavio - Business Wire